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ISPRS Int. J. Geo-Inf., Volume 8, Issue 1 (January 2019) – 51 articles

Cover Story (view full-size image): Geolocated big data paired with smaller (traditional) data opens new ways of understanding how people act and move around, and how heterogenous micro-activities crystallise into structural patterns. In this study, we extract mobile phone indicators from six months worth of Call Detail Records from French territories, and investigate their relation with socioeconomic data of urban organisation (deprivation, inequality and segregation). Some mobile phone indicators, such as the number of calls or the entropy of movements, relate significantly with the socioeconomic indicators of cities. However, most relations are sensitive (to the point of being reversed) to the way cities are defined. Cities delineated in a restricted way (central cores) tend to show weaker correlations than cities delineated as metropolitan areas and regions. View this paper.
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18 pages, 2552 KiB  
Article
The Spatial and Social Patterning of Property and Violent Crime in Toronto Neighbourhoods: A Spatial-Quantitative Approach
by Lu Wang, Gabby Lee and Ian Williams
ISPRS Int. J. Geo-Inf. 2019, 8(1), 51; https://doi.org/10.3390/ijgi8010051 - 21 Jan 2019
Cited by 28 | Viewed by 17902
Abstract
Criminal activities are often unevenly distributed over space. The literature shows that the occurrence of crime is frequently concentrated in particular neighbourhoods and is related to a variety of socioeconomic and crime opportunity factors. This study explores the broad patterning of property and [...] Read more.
Criminal activities are often unevenly distributed over space. The literature shows that the occurrence of crime is frequently concentrated in particular neighbourhoods and is related to a variety of socioeconomic and crime opportunity factors. This study explores the broad patterning of property and violent crime among different socio-economic stratums and across space by examining the neighbourhood socioeconomic conditions and individual characteristics of offenders associated with crime in the city of Toronto, which consists of 140 neighbourhoods. Despite being the largest urban centre in Canada, with a fast-growing population, Toronto is under-studied in crime analysis from a spatial perspective. In this study, both property and violent crime data sets from the years 2014 to 2016 and census-based Ontario-Marginalisation index are analysed using spatial and quantitative methods. Spatial techniques such as Local Moran’s I are applied to analyse the spatial distribution of criminal activity while accounting for spatial autocorrelation. Distance-to-crime is measured to explore the spatial behaviour of criminal activity. Ordinary Least Squares (OLS) linear regression is conducted to explore the ways in which individual and neighbourhood demographic characteristics relate to crime rates at the neighbourhood level. Geographically Weighted Regression (GWR) is used to further our understanding of the spatially varying relationships between crime and the independent variables included in the OLS model. Property and violent crime across the three years of the study show a similar distribution of significant crime hot spots in the core, northwest, and east end of the city. The OLS model indicates offender-related demographics (i.e., age, marital status) to be a significant predictor of both types of crime, but in different ways. Neighbourhood contextual variables are measured by the four dimensions of the Ontario-Marginalisation Index. They are significantly associated with violent and property crime in different ways. The GWR is a more suitable model to explain the variations in observed property crime rates across different neighbourhoods. It also identifies spatial non-stationarity in relationships. The study provides implications for crime prevention and security through an enhanced understanding of crime patterns and factors. It points to the need for safe neighbourhoods, to be built not only by the law enforcement sector but by a wide range of social and economic sectors and services. Full article
(This article belongs to the Special Issue GIS for Safety & Security Management)
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<p>Property and violent crime rates (per 100,000 persons) by Toronto neighbourhood, 2014–2016.</p> Full article ">Figure 2
<p>(<b>a</b>) LISA Statistics Map for Property Crime, 2014–2016. (<b>b</b>) LISA Statistics Map for Violent Crime, 2014–2016.</p> Full article ">Figure 3
<p>Distribution of adjusted local R<sup>2</sup> in GWR model for property crime.</p> Full article ">Figure 4
<p>Pseudo <span class="html-italic">t</span>-values for intercept and independent variables.</p> Full article ">Figure 5
<p>GWR local coefficients for intercept and independent variables.</p> Full article ">
19 pages, 6957 KiB  
Article
A Web Service-Oriented Geoprocessing System for Supporting Intelligent Land Cover Change Detection
by Huaqiao Xing, Jun Chen, Hao Wu and Dongyang Hou
ISPRS Int. J. Geo-Inf. 2019, 8(1), 50; https://doi.org/10.3390/ijgi8010050 - 20 Jan 2019
Cited by 13 | Viewed by 3991
Abstract
Remotely sensed imagery-based change detection is an effective approach for identifying land cover change information. A large number of change detection algorithms have been developed that satisfy different requirements. However, most change detection algorithms have been developed using desktop-based software in offline environments; [...] Read more.
Remotely sensed imagery-based change detection is an effective approach for identifying land cover change information. A large number of change detection algorithms have been developed that satisfy different requirements. However, most change detection algorithms have been developed using desktop-based software in offline environments; thus, it is increasingly difficult for common end-users, who have limited remote sensing experience and geographic information system (GIS) skills, to perform appropriate change detection tasks. To address this challenge, this paper proposes an online geoprocessing system for supporting intelligent land cover change detection (OGS-LCCD). This system leverages web service encapsulation technology and an automatic service composition approach to dynamically generate a change detection service chain. First, a service encapsulation strategy is proposed with an execution body encapsulation and service semantics description. Then, a constraint rule-based service composition method is proposed to chain several web services into a flexible change detection workflow. Finally, the design and implementation of the OGS-LCCD are elaborated. A step-by-step walk-through example for a web-based change detection task is presented using this system. The experimental results demonstrate the effectiveness and applicability of the prototype system. Full article
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<p>Process of encapsulating change detection algorithms into web processing services.</p> Full article ">Figure 2
<p>Preprocessing service selection to satisfy the execution constraint of service A.</p> Full article ">Figure 3
<p>Online geoprocessing system for supporting intelligent land cover change detection (OGS-LCCD) architecture.</p> Full article ">Figure 4
<p>Implementation of web processing service (WPS) service encapsulation from geoprocessing algorithms.</p> Full article ">Figure 5
<p>Unified modeling language (UML) sequence diagram of OGS-LCCD operation.</p> Full article ">Figure 6
<p>Uploading Landsat 5 data (for year 2010) using OGS-LCCD.</p> Full article ">Figure 7
<p>Uploading Landsat 8 data (for year 2018) using OGS-LCCD.</p> Full article ">Figure 8
<p>Service chain generation for change detection.</p> Full article ">Figure 9
<p>Monitoring of the execution of a change detection service chain.</p> Full article ">Figure 10
<p>Change detection results (purple regions) visualized using a web map service (WMS).</p> Full article ">Figure 11
<p>Two generated change detection service chains: (<b>a</b>) service chain based on the traditional method; (<b>b</b>) service chain based on the proposed method.</p> Full article ">Figure 12
<p>Actual implementation of the two service chains: (<b>a</b>) executed failed; (<b>b</b>) executed successfully.</p> Full article ">Figure 13
<p>Central processing unit (CPU) utilization (%).</p> Full article ">Figure 14
<p>Memory utilization (%).</p> Full article ">
12 pages, 13955 KiB  
Article
Small Manhole Cover Detection in Remote Sensing Imagery with Deep Convolutional Neural Networks
by Wei Liu, Dayu Cheng, Pengcheng Yin, Mengyuan Yang, Erzhu Li, Meng Xie and Lianpeng Zhang
ISPRS Int. J. Geo-Inf. 2019, 8(1), 49; https://doi.org/10.3390/ijgi8010049 - 19 Jan 2019
Cited by 23 | Viewed by 5361
Abstract
With the development of remote sensing technology and the advent of high-resolution images, obtaining data has become increasingly convenient. However, the acquisition of small manhole cover information still has shortcomings including low efficiency of manual surveying and high leakage rate. Recently, deep learning [...] Read more.
With the development of remote sensing technology and the advent of high-resolution images, obtaining data has become increasingly convenient. However, the acquisition of small manhole cover information still has shortcomings including low efficiency of manual surveying and high leakage rate. Recently, deep learning models, especially deep convolutional neural networks (DCNNs), have proven to be effective at object detection. However, several challenges limit the applications of DCNN in manhole cover object detection using remote sensing imagery: (1) Manhole cover objects often appear at different scales in remotely sensed images and DCNNs’ fixed receptive field cannot match the scale variability of such objects; (2) Manhole cover objects in large-scale remotely-sensed images are relatively small in size and densely packed, while DCNNs have poor localization performance when applied to such objects. To address these problems, we propose an effective method for detecting manhole cover objects in remotely-sensed images. First, we redesign the feature extractor by adopting the visual geometry group (VGG), which can increase the variety of receptive field size. Then, detection is performed using two sub-networks: a multi-scale output network (MON) for manhole cover object-like edge generation from several intermediate layers whose receptive fields match different object scales and a multi-level convolution matching network (M-CMN) for object detection based on fused feature maps, which combines several feature maps that enable small and densely packed manhole cover objects to produce a stronger response. The results show that our method is more accurate than existing methods at detecting manhole covers in remotely-sensed images. Full article
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<p>Architecture of our proposed method. Deep supervision is imposed at each side-output layer, guiding the side-outputs to obtain multi-level outputs. The subsequent fusion layer aids in learning how to combine outputs from multiple scales.</p> Full article ">Figure 2
<p>Details of the improved VGG16 architecture.</p> Full article ">Figure 3
<p>Different strategies for multi-level detection: (<b>a</b>) Prediction using multiple image scales with a single filter size; (<b>b</b>) Prediction using a single feature map with multiple filter sizes; (<b>c</b>) Prediction using multiple feature maps with multiple filter sizes.</p> Full article ">Figure 4
<p>Ground truth boxes.</p> Full article ">Figure 5
<p>Outputs of MON detection.</p> Full article ">Figure 6
<p>Manhole cover detection results with the proposed approach on three images.</p> Full article ">
16 pages, 8412 KiB  
Article
Consideration of Level of Confidence within Multi-Approach Satellite-Derived Bathymetry
by René Chénier, Ryan Ahola, Mesha Sagram, Marc-André Faucher and Yask Shelat
ISPRS Int. J. Geo-Inf. 2019, 8(1), 48; https://doi.org/10.3390/ijgi8010048 - 19 Jan 2019
Cited by 13 | Viewed by 4338
Abstract
The Canadian Hydrographic Service (CHS) publishes nautical charts covering all Canadian waters. Through projects with the Canadian Space Agency, CHS has been investigating remote sensing techniques to support hydrographic applications. One challenge CHS has encountered relates to quantifying its confidence in remote sensing [...] Read more.
The Canadian Hydrographic Service (CHS) publishes nautical charts covering all Canadian waters. Through projects with the Canadian Space Agency, CHS has been investigating remote sensing techniques to support hydrographic applications. One challenge CHS has encountered relates to quantifying its confidence in remote sensing products. This is particularly challenging with Satellite-Derived Bathymetry (SDB) where minimal in situ data may be present for validation. This paper proposes a level of confidence approach where a minimum number of SDB techniques are required to agree within a defined level to allow SDB estimates to be retained. The approach was applied to a Canadian Arctic site, incorporating four techniques: empirical, classification and photogrammetric (automatic and manual). Based on International Hydrographic Organization (IHO) guidelines, each individual approach provided results meeting the CATegory of Zones Of Confidence (CATZOC) level C requirement. By applying the level of confidence approach, where technique combinations agreed within 1 m (e.g., all agree, three agree, two agree) large portions of the extracted bathymetry could now meet the CATZOC A2/B requirement. Areas where at least three approaches agreed have an accuracy of 1.2 m and represent 81% of the total surface. The proposed technique not only increases overall accuracy but also removes some of the uncertainty associated with SDB, particularly for locations where in situ validation data is not available. This approach could provide an option for hydrographic offices to increase their confidence in SDB, potentially allowing for increased SDB use within hydrographic products. Full article
(This article belongs to the Special Issue Geo-Spatial Analysis in Hydrology)
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<p>Location of the Cambridge Bay study site on Victoria Island, Nunavut.</p> Full article ">Figure 2
<p>WorldView-2 stereo pair used for each Satellite Derived Bathymetry (SDB) technique. (<b>a</b>) Forward image, used for empirical and classification approaches. (<b>b</b>) Backward image, only used for photogrammetric techniques. Imagery © 2015, DigitalGlobe, Inc.</p> Full article ">Figure 3
<p>Spatial distribution of survey data up to 20 m in depth within Cambridge Bay. Note the wide coverage of the LiDAR survey relative to the multibeam datasets. Imagery © 2015, DigitalGlobe, Inc.</p> Full article ">Figure 4
<p>SDB results from the (<b>a</b>) manual photogrammetry, (<b>b</b>) classification, (<b>c</b>) empirical and (<b>d</b>) automatic photogrammetry techniques. Imagery © 2015, DigitalGlobe, Inc.</p> Full article ">Figure 5
<p>Results over dark features (e.g., underwater vegetation). (<b>a</b>) WorldView-2 image with underwater vegetation visible. (<b>b</b>) Empirical approach, (<b>c</b>) manual photogrammetric technique, (<b>d</b>) automatic photogrammetric technique and (<b>e</b>) classification approach. Note the greater depth variability in the empirical and classification results, likely due to dark feature presence. Imagery © 2015, DigitalGlobe, Inc.</p> Full article ">Figure 6
<p>Visualization of locations where four, three and two SDB techniques agree within 1 m. Imagery © 2015, DigitalGlobe, Inc.</p> Full article ">Figure 7
<p>Illustration of potential use of the level of confidence technique for quality control (QC). (<b>a</b>) Section of WorldView-2 imagery and (<b>b</b>) the associated number of matching SDB approaches. Note that the number of matching techniques is lower for dark bottom areas, likely due to vegetation presence. Specific techniques can be targeted for areas where two or fewer approaches agree if they can be expected to perform better for those locations (e.g., photogrammetric techniques for heterogeneous bottom areas). Imagery © 2015, DigitalGlobe, Inc.</p> Full article ">Figure 8
<p>Overview of the multi-approach SDB results. Imagery © 2015, DigitalGlobe, Inc.</p> Full article ">
16 pages, 52187 KiB  
Article
Deep Learning Segmentation and 3D Reconstruction of Road Markings Using Multiview Aerial Imagery
by Franz Kurz, Seyed Majid Azimi, Chun-Yu Sheu and Pablo d’Angelo
ISPRS Int. J. Geo-Inf. 2019, 8(1), 47; https://doi.org/10.3390/ijgi8010047 - 18 Jan 2019
Cited by 11 | Viewed by 6462
Abstract
The 3D information of road infrastructures is growing in importance with the development of autonomous driving. In this context, the exact 2D position of road markings as well as height information play an important role in, e.g., lane-accurate self-localization of autonomous vehicles. In [...] Read more.
The 3D information of road infrastructures is growing in importance with the development of autonomous driving. In this context, the exact 2D position of road markings as well as height information play an important role in, e.g., lane-accurate self-localization of autonomous vehicles. In this paper, the overall task is divided into an automatic segmentation followed by a refined 3D reconstruction. For the segmentation task, we applied a wavelet-enhanced fully convolutional network on multiview high-resolution aerial imagery. Based on the resulting 2D segments in the original images, we propose a successive workflow for the 3D reconstruction of road markings based on a least-squares line-fitting in multiview imagery. The 3D reconstruction exploits the line character of road markings with the aim to optimize the best 3D line location by minimizing the distance from its back projection to the detected 2D line in all the covering images. Results showed an improved IoU of the automatic road marking segmentation by exploiting the multiview character of the aerial images and a more accurate 3D reconstruction of the road surface compared to the semiglobal matching (SGM) algorithm. Further, the approach avoids the matching problem in non-textured image parts and is not limited to lines of finite length. In this paper, the approach is presented and validated on several aerial image data sets covering different scenarios like motorways and urban regions. Full article
(This article belongs to the Special Issue Innovative Sensing - From Sensors to Methods and Applications)
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<p>Appearance of road markings in aerial imagery (<b>a</b>–<b>c</b>) and corresponding parts of the digital surface model (DSM) (<b>d</b>–<b>f</b>).</p> Full article ">Figure 2
<p>(<b>Top row</b>) processing steps performed in the image space; (<b>bottom row</b>) processing steps in the object space.</p> Full article ">Figure 3
<p>Basic idea of line-based 3D refinement (<b>a</b>) before optimization and (<b>b</b>) after optimization and principle of a sliding window for the (<b>c</b>) first and (<b>d</b>) second node of a line.</p> Full article ">Figure 4
<p>(<b>a</b>,<b>b</b>) Examples of segmented road markings in pink and center lines in yellow generated by the skeleton operator. (<b>c</b>) Approximation points in object space with first and last point of an iteration in yellow, as well as the target point in red. (<b>d</b>) Reprojected approximation points into image space with search space for the search of corresponding line points (blue box). (<b>e</b>) Selected points in red in one image.</p> Full article ">Figure 5
<p>(<b>a</b>) DSM of a motorway surface. (<b>b</b>) Number of stereo pairs contributing to each DSM pixel. (<b>c</b>) Standard deviation of the height in meter for each DSM pixel.</p> Full article ">Figure 6
<p>Results of the DL road marking segmentation (magenta) in two selected overlapping images (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>) and (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>) of an image sequence. (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>) show the projected road markings in the object space with the number of contributing images (color coded). The darker, the more often the road marking was detected in the contributing images.</p> Full article ">Figure 7
<p>Details of the DL road marking segmentation in overlapping image pairs. (<b>a</b>,<b>e</b>) shows detected road marking in a construction zone; (<b>b</b>,<b>f</b>) markings of parking spaces are partly occluded in one image by a vehicle, false positives on the right of the parking lot are visible; (<b>c</b>,<b>g</b>) the truck on a motorway occludes a road marking in one image; (<b>d</b>,<b>h</b>) false positives caused by vehicles are not detected in the other image.</p> Full article ">Figure 8
<p>Results of the 3D refinement of road markings for four test sites. Left images (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) show the completeness of 3D refinement and right images (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) show a 3D view of the refined road marking points (blue dots) superimposed over the DSM. At green dots all requirements are fulfilled and the 3D refinement converges; the remaining road marking points are red.</p> Full article ">Figure 9
<p>Details of the 3D refinement: Many points at parking spaces and complex urban crossings do not fulfill the requirements, resulting in low completeness (<b>a</b>,<b>b</b>), almost all road markings point converge in the 3D refinement even at double dashed lane markings or at occlusions from vehicles. (<b>c</b>) completeness on motorways.</p> Full article ">Figure 10
<p>(<b>a</b>) 3D view of a planar road surface; refined 3D points are overlaid on the DSM. (<b>b</b>) Cross section of planar road surface, with six lanes showing refined 3D points in red and DSM surface in gray. (<b>c</b>) Root mean square (RMS) in height depending on the number of images used for the refinement.</p> Full article ">
16 pages, 8621 KiB  
Article
GEOBIA at the Terapixel Scale: Toward Efficient Mapping of Small Woody Features from Heterogeneous VHR Scenes
by François Merciol, Loïc Faucqueur, Bharath Bhushan Damodaran, Pierre-Yves Rémy, Baudouin Desclée, Fabrice Dazin, Sébastien Lefèvre, Antoine Masse and Christophe Sannier
ISPRS Int. J. Geo-Inf. 2019, 8(1), 46; https://doi.org/10.3390/ijgi8010046 - 18 Jan 2019
Cited by 14 | Viewed by 4593
Abstract
Land cover mapping has benefited a lot from the introduction of the Geographic Object-Based Image Analysis (GEOBIA) paradigm, that allowed to move from a pixelwise analysis to a processing of elements with richer semantic content, namely objects or regions. However, this paradigm requires [...] Read more.
Land cover mapping has benefited a lot from the introduction of the Geographic Object-Based Image Analysis (GEOBIA) paradigm, that allowed to move from a pixelwise analysis to a processing of elements with richer semantic content, namely objects or regions. However, this paradigm requires to define an appropriate scale, that can be challenging in a large-area study where a wide range of landscapes can be observed. We propose here to conduct the multiscale analysis based on hierarchical representations, from which features known as differential attribute profiles are derived over each single pixel. Efficient and scalable algorithms for construction and analysis of such representations, together with an optimized usage of the random forest classifier, provide us with a semi-supervised framework in which a user can drive mapping of elements such as Small Woody Features at a very large area. Indeed, the proposed open-source methodology has been successfully used to derive a part of the High Resolution Layers (HRL) product of the Copernicus Land Monitoring service, thus showing how the GEOBIA framework can be used in a big data scenario made of more than 38,000 Very High Resolution (VHR) satellite images representing more than 120 TB of data. Full article
(This article belongs to the Special Issue GEOBIA in a Changing World)
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<p>General flowchart of the proposed approach.</p> Full article ">Figure 2
<p>Multiscale approach.</p> Full article ">Figure 3
<p>Attribute Profile features extracted from inclusion the tree.</p> Full article ">Figure 4
<p>Pan-European map of the 38,000 scene footprints (red boxes) used for the Copernicus High Resolution Layer (HRL) Swall Woody Features (SWF) production. Two datasets are used for the experiments: (<b>a</b>) the 17 Worldview scenes over the LR61 study site (Germany, 10,200 km<sup>2</sup>) and (<b>b</b>) the 20 Pleiades scenes over the LR09 study site (Romania, 7000 km<sup>2</sup>). Scene illustrations are presented in false color composition: (Near infrared, Red, Green) as RGB.</p> Full article ">Figure 5
<p>Illustration of classification results: (<b>a</b>) false color composition of Pleiades scene #c55a over LR09 study site in Romania and (<b>b</b>) the same false color composition with superimposition of the Small Woody Features layer classification output (green).</p> Full article ">Figure 6
<p>Illustration of classification results: (<b>a</b>) false color composition of Worldview scene #400B over LR61 study site in Germany and (<b>b</b>) the same false color composition with superimposition of the Small Woody Features layer classification output (green).</p> Full article ">
18 pages, 5140 KiB  
Article
Deep Neural Networks and Kernel Density Estimation for Detecting Human Activity Patterns from Geo-Tagged Images: A Case Study of Birdwatching on Flickr
by Caglar Koylu, Chang Zhao and Wei Shao
ISPRS Int. J. Geo-Inf. 2019, 8(1), 45; https://doi.org/10.3390/ijgi8010045 - 18 Jan 2019
Cited by 19 | Viewed by 7241
Abstract
Thanks to recent advances in high-performance computing and deep learning, computer vision algorithms coupled with spatial analysis methods provide a unique opportunity for extracting human activity patterns from geo-tagged social media images. However, there are only a handful of studies that evaluate the [...] Read more.
Thanks to recent advances in high-performance computing and deep learning, computer vision algorithms coupled with spatial analysis methods provide a unique opportunity for extracting human activity patterns from geo-tagged social media images. However, there are only a handful of studies that evaluate the utility of computer vision algorithms for studying large-scale human activity patterns. In this article, we introduce an analytical framework that integrates a computer vision algorithm based on convolutional neural networks (CNN) with kernel density estimation to identify objects, and infer human activity patterns from geo-tagged photographs. To demonstrate our framework, we identify bird images to infer birdwatching activity from approximately 20 million publicly shared images on Flickr, across a three-year period from December 2013 to December 2016. In order to assess the accuracy of object detection, we compared results from the computer vision algorithm to concept-based image retrieval, which is based on keyword search on image metadata such as textual description, tags, and titles of images. We then compared patterns in birding activity generated using Flickr bird photographs with patterns identified using eBird data—an online citizen science bird observation application. The results of our eBird comparison highlight the potential differences and biases in casual and serious birdwatching, and similarities and differences among behaviors of social media and citizen science users. Our analysis results provide valuable insights into assessing the credibility and utility of geo-tagged photographs in studying human activity patterns through object detection and spatial analysis. Full article
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<p>Overview of the analytical workflow.</p> Full article ">Figure 2
<p>An example of YOLOv3 (You Only Look Once) objection detection result.</p> Full article ">Figure 3
<p>YOLO network architecture (adapted from [<a href="#B44-ijgi-08-00045" class="html-bibr">44</a>]).</p> Full article ">Figure 4
<p>Only YOLO detected bird photographs.</p> Full article ">Figure 5
<p>Only metadata detected bird photographs. (<b>a</b>) Acorn Woodpecker, (<b>b</b>) @fence #birdhouse #wood #lynnfriedman, (<b>c</b>) New piece! One if the largest paintings I have done of my birds! 30′′ × 40′′, and (<b>d</b>) #birdland #masnorioles.</p> Full article ">Figure 6
<p>Fixed-distance (20 miles) density of (<b>a</b>) YOLO-detected and (<b>b</b>) keyword search Flickr bird image counts.</p> Full article ">Figure 7
<p>Temporal patterns of YOLO-detected Flickr bird images and eBird observations.</p> Full article ">Figure 8
<p>Z-scores of the fixed-distance (20 miles) density of (<b>a</b>) YOLO-detected Flickr bird image and (<b>b</b>) eBird observations.</p> Full article ">Figure 9
<p>Percent of YOLO-detected Flickr bird images computed by an adaptive kernel based on a minimum threshold of 100 users that contain both Flickr and eBird users.</p> Full article ">
18 pages, 5093 KiB  
Article
TLS Measurement during Static Load Testing of a Railway Bridge
by Pelagia Gawronek and Maria Makuch
ISPRS Int. J. Geo-Inf. 2019, 8(1), 44; https://doi.org/10.3390/ijgi8010044 - 17 Jan 2019
Cited by 20 | Viewed by 5141
Abstract
Terrestrial laser scanning (TLS) technology has become increasingly popular in investigating displacement and deformation of natural and anthropogenic objects. Regardless of the accuracy of deformation identification, TLS provides remote comprehensive information about the measured object in a short time. These features of TLS [...] Read more.
Terrestrial laser scanning (TLS) technology has become increasingly popular in investigating displacement and deformation of natural and anthropogenic objects. Regardless of the accuracy of deformation identification, TLS provides remote comprehensive information about the measured object in a short time. These features of TLS were why TLS measurement was used for a static load test of an old, steel railway bridge. The results of the measurement using the Z + F Imager 5010 scanner and traditional surveying methods (for improved georeferencing) were compared to results of precise reflectorless tacheometry and precise levelling. The analyses involved various procedures for the determination of displacement from 3D data (black & white target analysis, point cloud analysis, and mesh surface analysis) and the need to pre-process the· 3D data was considered (georeferencing, automated filtering). The results demonstrate that TLS measurement can identify vertical displacement in line with the results of traditional measurements down to ±1 mm. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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<p>Tested bridge on railway line No. 098.</p> Full article ">Figure 2
<p>Static loading diagram for the bridge span; (<b>a</b>) lateral view; (<b>b</b>) bird-eye view.</p> Full article ">Figure 3
<p>Measurement conditions; (<b>a</b>) situation; (<b>b</b>) levelled points on the bridge span; (<b>c</b>) the elements of controlled points network measured using tacheometry.</p> Full article ">Figure 4
<p>Spatial displacement distribution functions of two independent samples TACH vs. TLS; (<b>a</b>) along the OX axis, (<b>b</b>) along the OY axis, (<b>c</b>) vertical.</p> Full article ">Figure 5
<p>Results of point cloud analysis (2); (<b>a</b>) set (A) of TLS data, (<b>b</b>) set (B) of TLS data, (<b>c</b>) set (C) of TLS data.</p> Full article ">Figure 6
<p>Displacement values from point cloud analysis (2) and precise levelling (LEV) [mm].</p> Full article ">Figure 7
<p>Results of mesh surface analysis (3); (<b>a</b>) set (B) of TLS data, (<b>b</b>) set (C) of TLS data.</p> Full article ">Figure 8
<p>Plot of displacement values from mesh surface analysis (3) and precise levelling (LEV) [mm].</p> Full article ">
24 pages, 4835 KiB  
Article
Recent NDVI Trends in Mainland Spain: Land-Cover and Phytoclimatic-Type Implications
by Carlos J. Novillo, Patricia Arrogante-Funes and Raúl Romero-Calcerrada
ISPRS Int. J. Geo-Inf. 2019, 8(1), 43; https://doi.org/10.3390/ijgi8010043 - 17 Jan 2019
Cited by 31 | Viewed by 5964
Abstract
The temporal evolution of vegetation is one of the best indicators of climate change, and many earth system models are dependent on an accurate understanding of this process. However, the effect of climate change is expected to vary from one land-cover type to [...] Read more.
The temporal evolution of vegetation is one of the best indicators of climate change, and many earth system models are dependent on an accurate understanding of this process. However, the effect of climate change is expected to vary from one land-cover type to another, due to the change in vegetation and environmental conditions. Therefore, it is pertinent to understand the effect of climate change by land-cover type to understand the regions that are most vulnerable to climate change. Hence, in this study we analyzed the temporal statistical trends (2001–2016) of the MODIS13Q1 normalized difference vegetation index (NDVI) to explore whether there are differences, by land-cover class and phytoclimatic type, in mainland Spain and the Balearic Islands. We found 7.6% significant negative NDVI trends and 11.8% significant positive NDVI trends. Spatial patterns showed a non-random distribution. The Atlantic biogeographical region showed an unexpected 21% significant negative NDVI trends, and the Alpine region showed only 3.1% significant negative NDVI trends. We also found statistical differences between NDVI trends by land cover and phytoclimatic type. Variance explained by these variables was up to 35%. Positive trends were explained, above all, by land occupations, and negative trends were explained by phytoclimates. Warmer phytoclimatic classes of every general type and forest, as well as some agriculture land covers, showed negative trends. Full article
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<p>Map of our study area with biogeographical regions (Mapa de regiones biogeográficas estatal; © Ministerio para la Transición Ecológica (MITECO)).</p> Full article ">Figure 2
<p>Map of Co-Ordination of Information on the Environment (CORINE) Land Cover (CLC) 2006 classes at level 1 of disaggregation from our study area.</p> Full article ">Figure 3
<p>Map of Allué phytoclimatic types or zones in mainland Spain and the Balearic Islands.</p> Full article ">Figure 4
<p>Data processing scheme. TS refers to Theil–Sen slope data and MK refers to the Mann–Kendall test.</p> Full article ">Figure 5
<p>Spatial pattern that resulted from intersecting the Theil–Sen slopes with the Mann–Kendall test (<span class="html-italic">z</span>-statistic), testing annual averages from 2001–2016 Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) values. The red color shows the pixels with negative Theil–Sen slopes and a Mann–Kendall <span class="html-italic">z</span>-statistic below 0.1 (significant trend). The green color shows the pixels with positive Theil–Sen slopes and a Mann–Kendall <span class="html-italic">z</span>-statistic below 0.1 (significant trend). Finally, the black color shows the pixels with a Mann–Kendall <span class="html-italic">z</span>-statistic higher than 0.1 (non-significant trend).</p> Full article ">Figure 6
<p>Interval plots (95% confidence interval (CI)) from the variance analyses. The <span class="html-italic">x</span>-axis labels are the Allué phytoclimatic regions (<b>a</b>) and the CORINE Land-Cover code; (<b>b</b>) the <span class="html-italic">y</span>-axis shows the percentage of significant (<span class="html-italic">p</span> &lt; 0.1) trend pixels.</p> Full article ">Figure 7
<p>Interval plots (95% CI) from the variance analyses. The <span class="html-italic">x</span>-axis labels are the Allué phytoclimatic regions (<b>a</b>) and the CLC codes; (<b>b</b>) the <span class="html-italic">y</span>-axis shows the percentage of significant (<span class="html-italic">p</span> &lt; 0.1) positive (in green color) and negative (in red color) trend pixels.</p> Full article ">Figure 8
<p>Percentage of pixels with a significant trend by CORINE Land-Cover class (<b>a</b>) and by phytoclimatic type (<b>b</b>). Negative is represented by negative (in red color) and positive is green in color. Lines are mean values. Difference in percentage (<span class="html-italic">y</span>-axis) between the observed and expected negative NDVI, with negative (in red color) and positive (in green color) short-term trend pixels from our study area by CORINE Land-Cover class (<b>c</b>) and by phytoclimatic type (<b>d</b>).</p> Full article ">Figure 9
<p>Difference in percentage (<span class="html-italic">y</span>-axis) between the observed and expected NDVI negative (in red color) and positive (in green color) short-term trend pixels from our study area, by phytoclimatic type, within the 311 (<b>a</b>), the 312 (<b>b</b>), the 313 (<b>c</b>), and the 321 (<b>d</b>) CORINE Land-Cover classes.</p> Full article ">
13 pages, 3990 KiB  
Article
Relationship between Winter Snow Cover Dynamics, Climate and Spring Grassland Vegetation Phenology in Inner Mongolia, China
by Dejing Qiao and Nianqin Wang
ISPRS Int. J. Geo-Inf. 2019, 8(1), 42; https://doi.org/10.3390/ijgi8010042 - 17 Jan 2019
Cited by 29 | Viewed by 4525
Abstract
The onset date of spring phenology (SOS) is regarded as a key parameter for understanding and modeling vegetation–climate interactions. Inner Mongolia has a typical temperate grassland vegetation ecosystem, and has a rich snow cover during winter. Due to climate change, the winter snow [...] Read more.
The onset date of spring phenology (SOS) is regarded as a key parameter for understanding and modeling vegetation–climate interactions. Inner Mongolia has a typical temperate grassland vegetation ecosystem, and has a rich snow cover during winter. Due to climate change, the winter snow cover has undergone significant changes that will inevitably affect the vegetation growth. Therefore, improving our ability to accurately describe the responses of spring grassland vegetation phenology to winter snow cover dynamics would enhance our understanding of changes in terrestrial ecosystems due to their responses to climate changes. In this study, we quantified the spatial-temporal change of SOS by using the Advanced Very High Resolution Radiometer (AVHRR) derived Normalized Difference Vegetation Index (NDVI) from 1982 to 2015, and explored the relationships between winter snow cover, climate, and SOS across different grassland vegetation types. The results showed that the SOS advanced significantly at a rate of 0.3 days/year. Winter snow cover dynamics presented a significant positive correlation with the SOS, except for the start date of snow cover. Moreover, the relationship with the increasing temperature and precipitation showed a significant negative correlation, except that increasing Tmax (maximum air temperature) and Tavg (average air temperature) would lead a delay in SOS for desert steppe ecosystems. Sunshine hours and relative humidity showed a weaker correlation. Full article
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<p>(<b>a</b>) The geographic location of Inner Mongolia; (<b>b</b>) the distribution of grassland vegetation types; and (<b>c</b>) the elevation, weather stations and phenology observation stations of Inner Mongolia.</p> Full article ">Figure 2
<p>A schematic diagram illustrates the retrieval of spring phenology using the logistic fitting method [<a href="#B45-ijgi-08-00042" class="html-bibr">45</a>]. The solid line indicates the fitted logistic curve and the dashed line is the rate of change in curvature of the fitted logistic curve. Onset date of spring phenology (SOS) is defined as the first local maximum of the dashed curve. The red line indicates that the vegetation index begin to increase rapidly.</p> Full article ">Figure 3
<p>(<b>a</b>) Spatial distribution of the mean SOS in the grassland of Inner Mongolia during 1982–2015; (<b>b</b>) spatial distribution of the change trend for SOS in the grassland of Inner Mongolia during 1982–2015. The SOS change trends are termed significant for pixels at <span class="html-italic">p</span> &lt; 0.05 level.</p> Full article ">Figure 4
<p>34 year average of SOS and its change trends for different vegetation types: (<b>a</b>) meadow steppe, (<b>b</b>) typical steppe, and (<b>c</b>) desert steppe. Each bin represents a 10 day range of SOS. The height and color of each bin indicate the number and fitting slope (i.e., the change trend) of the pixels that fall within the bin, respectively, with the color bar of the slope on the bottom of the figure. Only statistically significant trends (<span class="html-italic">p</span> &lt; 0.05) are included.</p> Full article ">Figure 5
<p>The change trends of SOS for different vegetation types with different window lengths (i.e., study period): (<b>a</b>) meadow steppe; (<b>b</b>) typical steppe and (<b>c</b>) desert steppe. Each dot represents mean trends of each vegetation type over a single window size (1–33 years) for each start year (1982–2014). Only statistically significant trends (<span class="html-italic">p</span> &lt; 0.05) are included in this analysis. The red (green) color indicates positive (negative) trends.</p> Full article ">Figure 6
<p>The interannual variability of each climate factor in different grassland types. The value in each grid indicates the rate of change for each climate factor. Values are also color-coded, with blue indicating negative values and red indicating positive values. The color of each grid corresponds to the value, with the color bar on the right of the table. The asterisks (*) indicate the climate factors trends that are statistically significant at the <span class="html-italic">p</span> &lt; 0.1 level, and the double asterisks (**) indicate significance at the <span class="html-italic">p</span> &lt; 0.05 level.</p> Full article ">Figure 7
<p>The correlation between SOS and climate factors for three vegetation types: (<b>a</b>) meadow steppe; (<b>b</b>) typical steppe; and (<b>c</b>) desert steppe. Each dot represents the correlation between SOS and the corresponding climate factor, with the red color indicating a positive correlation and blue color indicating a negative correlation. The asterisks (*) indicate the trends of the climate factors trends that are statistically significant with <span class="html-italic">p</span> &lt; 0.1 and the double asterisks (**) indicate trends with <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">
29 pages, 5496 KiB  
Article
Role-Tailored Map Dashboards—A New Approach for Enhancing the Forest-based Supply Chain
by Caroline Atzl, Johannes Scholz, Bernhard Vockner, Manfred Mittlböck and Laura Knoth
ISPRS Int. J. Geo-Inf. 2019, 8(1), 41; https://doi.org/10.3390/ijgi8010041 - 17 Jan 2019
Cited by 2 | Viewed by 5417
Abstract
The article presents a map dashboard aimed at enhancing the information flow in the forest-based supply chain (FbSC). We especially focus on the procurement stage and connect the stakeholders in (near) real-time via standardized data models, interfaces and services, as well as using [...] Read more.
The article presents a map dashboard aimed at enhancing the information flow in the forest-based supply chain (FbSC). We especially focus on the procurement stage and connect the stakeholders in (near) real-time via standardized data models, interfaces and services, as well as using open-source software only. For the communication strategy, we use a new approach that incorporates the user’s roles and tasks to create role-tailored views on the dashboard showing specific task-oriented web maps. Hence, the first research question aims at identifying the roles and tasks in Austrian forestry. We identified four major roles (site managers & foresters, forest workers, truck drivers, customers) and six tasks during group discussions. The second research question deals with the effects of a role-tailored map dashboard. Therefore, we evaluated the prototype in a two-week test phase that concludes with a field study with five experts. The results are twofold: qualitative using the results from field interviews and quantitative based on a now vs. then comparison with regard to the number of media disruptions. This comparison reveals that up to 80% of the media disruption in our use case scenario could be removed by using the role-tailored map dashboard. Full article
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<p>Technical open-source workflow for creating a role-tailored map dashboard.</p> Full article ">Figure 2
<p>Two exemplary start pages showing (<b>a</b>) the view for the site managers, and (<b>b</b>) the view for the truck drivers using the task-oriented design metaphor.</p> Full article ">Figure 3
<p>Passive and active components of the role-tailored map dashboard with regard to the ISA-95 standard levels (based on [<a href="#B64-ijgi-08-00041" class="html-bibr">64</a>]).</p> Full article ">Figure 4
<p>The four results with regard to the research questions: (<b>a</b>) the identified major roles and tasks, (<b>b</b>) some of the final web mapping prototypes, (<b>c</b>) evaluation of the prototype, and (<b>d</b>) the qualitative and quantitative effects of the role-tailored map dashboard prototype.</p> Full article ">Figure 5
<p>Identified major roles, their tasks and the corresponding web mapping apps.</p> Full article ">Figure 6
<p>Exemplary use case scenario of how the involved roles interact with the prototype.</p> Full article ">Figure 7
<p>The “Harvester/forwarder app” for managing woodpile locations is one of the five web mapping apps of the role-tailored map dashboard.</p> Full article ">Figure 8
<p>The “Driver app” is used for managing deliveries and here, the planned deliveries for a specific woodpile storage location are shown.</p> Full article ">Figure 9
<p>The “Freight exchange app” showing active and consumed delivery notes for different customers (sawmills).</p> Full article ">Figure 10
<p>The “Monitoring app” showing the last 4 h (including tracks, truck states, barriers, environmental information).</p> Full article ">Figure 11
<p>Media disruptions within the FbSC processes showing the actual state (now).</p> Full article ">Figure 12
<p>Reduction of media disruptions within the FbSC processes by using the role-tailored map dashboard prototype (then).</p> Full article ">
17 pages, 8210 KiB  
Article
Semi-Automatic Versus Manual Mapping of Cold-Water Coral Carbonate Mounds Located Offshore Norway
by Alexandra Jarna, Nicole J. Baeten, Sigrid Elvenes, Valérie K. Bellec, Terje Thorsnes and Markus Diesing
ISPRS Int. J. Geo-Inf. 2019, 8(1), 40; https://doi.org/10.3390/ijgi8010040 - 16 Jan 2019
Cited by 6 | Viewed by 4498
Abstract
Cold-water coral reefs are hotspots of biological diversity and play an important role as carbonate factories in the global carbon cycle. Reef-building corals can be found in cold oceanic waters around the world. Detailed knowledge on the spatial location and distribution of coral [...] Read more.
Cold-water coral reefs are hotspots of biological diversity and play an important role as carbonate factories in the global carbon cycle. Reef-building corals can be found in cold oceanic waters around the world. Detailed knowledge on the spatial location and distribution of coral reefs is of importance for spatial management, conservation and science. Carbonate mounds (reefs) are readily identifiable in high-resolution multibeam echosounder data but systematic mapping programs have relied mostly on visual interpretation and manual digitizing so far. Developing more automated methods will help to reduce the time spent on this laborious task and will additionally lead to more objective and reproducible results. In this paper, we present an attempt at testing whether rule-based classification can replace manual mapping when mapping cold-water coral carbonate mounds. To that end, we have estimated and compared the accuracies of manual mapping, pixel-based terrain analysis and object-based image analysis. To verify the mapping results, we created a reference dataset of presence/absence points agreed upon by three mapping experts. There were no statistically significant differences in the overall accuracies of the maps produced by the three approaches. We conclude that semi-automated rule-based methods might be a viable option for mapping carbonate mounds with high spatial detail over large areas. Full article
(This article belongs to the Special Issue GEOBIA in a Changing World)
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<p>Flowchart summarizing the experiment.</p> Full article ">Figure 2
<p>(<b>a</b>) Location of study area located on Skjoldryggen on the Mid-Norwegian continental shelf. (<b>b</b>) Close-up of study area; also shown are bioclastic sediments mapped by the MAREANO program as a proxy for CWC carbonate mounds [<a href="#B26-ijgi-08-00040" class="html-bibr">26</a>]. (<b>c</b>) Area presented later in Figures 4 and 5. (<b>d</b>) Area presented later in <a href="#ijgi-08-00040-f003" class="html-fig">Figure 3</a>. (<b>e</b>) Area presented later in Figure 8. Elongated carbonate mounds can be seen growing in a NW–SE direction on a surface furrowed by iceberg plough marks (<b>c</b>–<b>e</b>).</p> Full article ">Figure 3
<p>Different derivatives overlaid by manually mapped carbonate mounds (in green): (<b>a</b>) BPI20, (<b>b</b>) slope, (<b>c</b>) plan curvature and (<b>d</b>) profile curvature.</p> Full article ">Figure 4
<p>Flowchart summarizing the process of pixel-based terrain analysis.</p> Full article ">Figure 5
<p>Classified polygons based on BPI3. (<b>a</b>) Classified polygons without buffer. (<b>b</b>) Classified polygons after applying 10-m buffer zone.</p> Full article ">Figure 6
<p>Flowchart summarizing the process of GEOBIA.</p> Full article ">Figure 7
<p>(<b>a</b>) Close-up of the semi-automatic classification exported from eCognition and (<b>b</b>) close-up result of semi-automatic classification with 5-m buffer in orange used for comparison.</p> Full article ">Figure 8
<p>(<b>a</b>) The distribution of reference points in the whole study area. (<b>b</b>) Example with definition of mounds based on merging all 3 classifications into one for random selection of reference points dataset.</p> Full article ">Figure 9
<p>Comparison of overall accuracy (PCC) mean (dot) and 95% confidence intervals (whiskers) for the three methods.</p> Full article ">Figure 10
<p>Area showing the distribution of reference points and overlapping of all three classifications together with bioclastic sediments [<a href="#B26-ijgi-08-00040" class="html-bibr">26</a>].</p> Full article ">
0 pages, 13990 KiB  
Article
RETRACTED: Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification
by Zhiqiang Li, Chengqi Cheng, Mei-Po Kwan, Xiaochong Tong and Shaohong Tian
ISPRS Int. J. Geo-Inf. 2019, 8(1), 39; https://doi.org/10.3390/ijgi8010039 - 16 Jan 2019
Cited by 43 | Viewed by 7874 | Retraction
Abstract
Asphalt pavement ages and incurs various distresses due to natural and human factors. Thus, it is crucial to rapidly and accurately extract different types of pavement distress to effectively monitor road health status. In this study, we explored the feasibility of pavement distress [...] Read more.
Asphalt pavement ages and incurs various distresses due to natural and human factors. Thus, it is crucial to rapidly and accurately extract different types of pavement distress to effectively monitor road health status. In this study, we explored the feasibility of pavement distress identification using low-altitude unmanned aerial vehicle light detection and ranging (UAV LiDAR) and random forest classification (RFC) for a section of an asphalt road that is located in the suburb of Shihezi City in Xinjiang Province of China. After a spectral and spatial feature analysis of pavement distress, a total of 48 multidimensional and multiscale features were extracted based on the strength of the point cloud elevations and reflection intensities. Subsequently, we extracted the pavement distresses from the multifeature dataset by utilizing the RFC method. The overall accuracy of the distress identification was 92.3%, and the kappa coefficient was 0.902. When compared with the maximum likelihood classification (MLC) and support vector machine (SVM), the RFC had a higher accuracy, which confirms its robustness and applicability to multisample and high-dimensional data classification. Furthermore, the method achieved an overall accuracy of 95.86% with a validation dataset. This result indicates the validity and stability of our method, which highway maintenance agencies can use to evaluate road health conditions and implement maintenance. Full article
(This article belongs to the Special Issue Applications and Potential of UAV Photogrammetric Survey)
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<p>Research roadmap.</p> Full article ">Figure 2
<p>Study Area.</p> Full article ">Figure 3
<p>Light detection and ranging (LiDAR) data acquisition system.</p> Full article ">Figure 4
<p>Direction angle variation in the POS system of an unmanned aerial vehicle (UAV) flight.</p> Full article ">Figure 5
<p>Point cloud filtering. <b>(a)</b> The original point cloud; and, <b>(b)</b> the pavement point cloud.</p> Full article ">Figure 6
<p>Triangulated irregular network (TIN) model of the road section in the study area.</p> Full article ">Figure 7
<p>Definition of roughness.</p> Full article ">Figure 8
<p>Distribution of the roughness index on microscopic scales.</p> Full article ">Figure 9
<p>Distribution of the roughness index on macroscopic scales.</p> Full article ">Figure 10
<p>Distribution of Gaussian curvature on microscopic scales.</p> Full article ">Figure 11
<p>Distribution of Gaussian curvature on macroscopic scales.</p> Full article ">Figure 12
<p>Reflection intensity interpolated image.</p> Full article ">Figure 13
<p>Object-oriented segmentation results.</p> Full article ">Figure 14
<p>Regional features of objects after segmentation.</p> Full article ">Figure 15
<p>Shape features of objects after segmentation.</p> Full article ">Figure 16
<p>Distribution of the training samples.</p> Full article ">Figure 17
<p>Results of several classification methods.</p> Full article ">Figure 18
<p>Pavement distress classification results of the validation data.</p> Full article ">
14 pages, 4078 KiB  
Article
Automated Matching of Multi-Scale Building Data Based on Relaxation Labelling and Pattern Combinations
by Yunfei Zhang, Jincai Huang, Min Deng, Chi Chen, Fangbin Zhou, Shuchun Xie and Xiaoliang Fang
ISPRS Int. J. Geo-Inf. 2019, 8(1), 38; https://doi.org/10.3390/ijgi8010038 - 16 Jan 2019
Cited by 10 | Viewed by 3914
Abstract
With the increasingly urgent demand for map conflation and timely data updating, data matching has become a crucial issue in big data and the GIS community. However, non-rigid deviation, shape homogenization, and uncertain scale differences occur in crowdsourced and official building data, causing [...] Read more.
With the increasingly urgent demand for map conflation and timely data updating, data matching has become a crucial issue in big data and the GIS community. However, non-rigid deviation, shape homogenization, and uncertain scale differences occur in crowdsourced and official building data, causing challenges in conflating heterogeneous building datasets from different sources and scales. This paper thus proposes an automated building data matching method based on relaxation labelling and pattern combinations. The proposed method first detects all possible matching objects and pattern combinations to create a matching table, and calculates four geo-similarities for each candidate-matching pair to initialize a probabilistic matching matrix. After that, the contextual information of neighboring candidate-matching pairs is explored to heuristically amend the geo-similarity-based matching matrix for achieving a contextual matching consistency. Three case studies are conducted to illustrate that the proposed method obtains high matching accuracies and correctly identifies various 1:1, 1:M, and M:N matching. This indicates the pattern-level relaxation labelling matching method can efficiently overcome the problems of shape homogeneity and non-rigid deviation, and meanwhile has weak sensitivity to uncertain scale differences, providing a functional solution for conflating crowdsourced and official building data. Full article
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<p>Complex difficulties in matching multi-scale building polygons: (<b>a</b>) shape homogenization and non-rigid deviations between building features; (<b>b</b>) 1:M and M:N matching caused by inconsistent levels of detail (LODs).</p> Full article ">Figure 2
<p>The relaxation labelling matching model in consideration of individual objects and pattern combinations: (<b>a</b>) graphical representation; (<b>b</b>) matrix representation.</p> Full article ">Figure 3
<p>The possible matching combinations of one building object <span class="html-italic">r<sub>i</sub></span>: (<b>a</b>) one building object <span class="html-italic">r<sub>i</sub></span> and its candidate-matching objects <span class="html-italic">t</span><sub>1</sub>…<span class="html-italic">t</span><sub>4</sub>; (<b>b</b>) <span class="html-italic">r<sub>i</sub></span> matching with none; (<b>c</b>) <span class="html-italic">r<sub>i</sub></span> matching with one object; (<b>d</b>) <span class="html-italic">r<sub>i</sub></span> matching with two objects; (<b>e</b>) <span class="html-italic">r<sub>i</sub></span> matching with three objects; (<b>f</b>) <span class="html-italic">r<sub>i</sub></span> matching with four objects.</p> Full article ">Figure 4
<p>Aggregating neighboring candidate-matching objects into pattern combinations: (<b>a</b>) aggregating two or more objects based on centroid distances; (<b>b</b>) aggregating two objects based on convex hull approximation.</p> Full article ">Figure 5
<p>Calculation of absolute geometric similarity and the relative compatibility coefficient: (<b>a</b>) absolute geometric similarity between candidate-matching pairs; (<b>b</b>) relative compatibility coefficient between neighboring candidate-matching pairs. MER: minimum enclosing rectangle.</p> Full article ">Figure 6
<p>Integrating the sub-support indexes of neighboring individual objects and pattern combinations into a total support index: (<b>a</b>) neighboring individual objects and pattern combinations of (<span class="html-italic">r<sub>i</sub></span>, <span class="html-italic">t<sub>j</sub></span>); (<b>b</b>) integrating all sub-support indexes into a total support index.</p> Full article ">Figure 7
<p>Matching results comparison of the mock building data: (<b>a</b>) the initial matching pairs based on local geometric similarity; (<b>b</b>) the identified matching pairs by the object-level relaxation labelling method; (<b>c</b>) the identified matching pairs by the pattern-level relaxation labelling method.</p> Full article ">Figure 8
<p>Matching results of the Xi’an and Dallas data: (<b>a</b>) the matching results for the Xi’an data with obvious scale differences; (<b>b</b>) the matching results for the Dallas data with uncertain scale differences.</p> Full article ">Figure 9
<p>Matching probability change during the relaxation labelling process: (<b>a</b>) probability change of candidate-matching pairs of object #125; (<b>b</b>) probability change of candidate-matching pairs of object #26.</p> Full article ">Figure 10
<p>Comparing the probabilities of matching with an individual object and simultaneously matching with multiple objects: (<b>a</b>) object #81 and #17 in <span class="html-italic">R</span> and their candidate-matching objects in <span class="html-italic">T</span>; (<b>b</b>) the matching probability comparison of all candidate-matching pairs of #81; (<b>c</b>) the matching probability comparison of all candidate-matching pairs of #17.</p> Full article ">
21 pages, 14246 KiB  
Article
Threat of Pollution Hotspots Reworking in River Systems: Case Study of the Ploučnice River (Czech Republic)
by Jitka Elznicová, Tomáš Matys Grygar, Jan Popelka, Martin Sikora, Petr Novák and Michal Hošek
ISPRS Int. J. Geo-Inf. 2019, 8(1), 37; https://doi.org/10.3390/ijgi8010037 - 16 Jan 2019
Cited by 6 | Viewed by 4139
Abstract
As fluvial pollution may endanger the quality of water and solids transported by rivers, mapping and evaluation of historically polluted fluvial sediments is an urgent topic. The Ploučnice River and its floodplain were polluted by local uranium mining from 1971–1989. We have studied [...] Read more.
As fluvial pollution may endanger the quality of water and solids transported by rivers, mapping and evaluation of historically polluted fluvial sediments is an urgent topic. The Ploučnice River and its floodplain were polluted by local uranium mining from 1971–1989. We have studied this river since 2013 using a combination of diverse methods, including geoinformatics, to identify pollution hotspots in floodplains and to evaluate the potential for future reworking. Archival information on pollution history and past flooding was collected to understand floodplain dynamics and pollution heterogeneity. Subsequently, a digital terrain model based on laser scanning data and data analysis were used to identify the sites with river channel shifts. Finally, non-invasive geochemical mapping was employed, using portable X-ray fluorescence and gamma spectrometers. The resulting datasets were processed with geostatistical tools. One of the main outputs of the study was a detailed map of pollution distribution in the floodplain. The results showed a relationship between polluted sediment deposition, past channel shifts and floodplain development. We found that increased concentration of pollution occurred mainly in the cut-off meanders and lateral channel deposits from the mining period, the latter in danger of reworking (reconnecting to the river) in the coming decades. Full article
(This article belongs to the Special Issue GIS for Safety & Security Management)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Map of the study areas in this research (red) and other studied areas (yellow). The acronyms of the study sites are the initials of students who worked there.</p> Full article ">Figure 2
<p>Stable Cadastre maps and archive orthophotos of the study areas.</p> Full article ">Figure 3
<p>Mapping instruments in the MS-West of Hradčany study area: Low resolution gamma activity survey (<b>A</b>), drone and detail of a high-resolution image (<b>B</b>), gamma spectrometer and measuring points (<b>C</b>), and X-ray fluorescence (XRF) spectrometer and measuring points (<b>D</b>).</p> Full article ">Figure 4
<p>Estimated optimal variogram functions (blue lines) for U (<b>left</b> panels) and Zn (<b>right</b> panels) in the study areas.</p> Full article ">Figure 5
<p>Historical channel positions and interpreted geomorphologic features in the study areas: (<b>A</b>) MS-West of Hradčany, (<b>B</b>) AT1-Hradčany, and (<b>C</b>) MH-Boreček.</p> Full article ">Figure 6
<p>The MS-West of Hradčany study area. Digital terrain model and an aerial photograph (panel <b>A</b>), gamma activity map (panel <b>B</b>) and XRF maps of U (panel <b>C</b>), the U/Fe ratio (panel <b>D</b>), Zn (panel <b>E</b>) and the Zn/Fe ratio (panel <b>F</b>). Historical channel positions are also shown. Inset in panel <b>A</b> shows an aerial photograph of the tree remnant in the channel.</p> Full article ">Figure 7
<p>Maps of Al/Si (<b>A</b>) and Zr/Rb (<b>B</b>) ratios in the MS-West of Hradčany study area. Blue arrows indicate enhanced transport paths of overbank clastics as extrapolation of flow directions; they point to study areas with coarser overbank sediments, where gamma activity and XRF mapping produced markedly different results.</p> Full article ">Figure 8
<p>The AT1-Hradčany study area. Digital terrain model (panel <b>A</b>), gamma activity map (panel <b>B</b>), XRF maps of U (panel <b>C</b>) and the U/Fe ratio (panel <b>D</b>), and XRF maps of Zn (panel <b>E</b>) and the Zn/Fe ratio (panel <b>F</b>). Historical channel positions are also shown.</p> Full article ">Figure 9
<p>The MH–Boreček study area. Digital terrain model (panel <b>A</b>), gamma activity map (panel <b>B</b>), XRF maps of U (panel <b>C</b>) and the U/Fe ratio (panel <b>D</b>), and XRF maps of Zn (panel <b>E</b>) and the Zn/Fe ratio (panel <b>F</b>). Historical channel positions are also shown.</p> Full article ">
13 pages, 4072 KiB  
Article
Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images
by Bingxin Bai, Yumin Tan, Dong Guo and Bo Xu
ISPRS Int. J. Geo-Inf. 2019, 8(1), 36; https://doi.org/10.3390/ijgi8010036 - 16 Jan 2019
Cited by 14 | Viewed by 3910
Abstract
Time series remote sensing images can be used to monitor the dynamic changes of forest lands. Due to consistent cloud cover and fog, a single sensor typically provides limited data for dynamic monitoring. This problem is solved by combining observations from multiple sensors [...] Read more.
Time series remote sensing images can be used to monitor the dynamic changes of forest lands. Due to consistent cloud cover and fog, a single sensor typically provides limited data for dynamic monitoring. This problem is solved by combining observations from multiple sensors to form a time series (a satellite image time series). In this paper, the pixel-based multi-source remote sensing image fusion (MulTiFuse) method is applied to combine the Landsat time series and Huanjing-1 A/B (HJ-1 A/B) data in the Fuling district of Chongqing, China. The fusion results are further corrected and improved with spatial features. Dynamic monitoring and analysis of the study area are subsequently performed on the improved time series data using the combination of Mann-Kendall trend detection method and Theil Sen Slope analysis. The monitoring results show that a majority of the forest land (60.08%) has experienced strong growth during the 1999–2013 period. Accuracy assessment indicates that the dynamic monitoring using the fused image time series produces results with relatively high accuracies. Full article
(This article belongs to the Special Issue Multi-Source Geoinformation Fusion)
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<p>Study area.</p> Full article ">Figure 2
<p>Data processing and analysis flow chart.</p> Full article ">Figure 3
<p>Original and interpolated hypothesized time series (adapted from <a href="#ijgi-08-00036-f004" class="html-fig">Figure 4</a> on page 281 in the paper by J. Reiche et al [<a href="#B11-ijgi-08-00036" class="html-bibr">11</a>]).</p> Full article ">Figure 4
<p>Comparison of remote sensing images (display in standard false color) before and after fusion: (<b>a</b>) HJ-1 B (2009-09-30); (<b>b</b>) Landsat 7 (2009-08-25); (<b>c</b>) Fused image.</p> Full article ">Figure 5
<p>Trend of normalized difference vegetation index (NDVI) changes in Fuling District, Chongqing, 1999–2013.</p> Full article ">Figure 6
<p>Geographic distribution of woodland growth trend in Fuling District, Chongqing, 1999–2013.</p> Full article ">Figure 7
<p>Forest attenuation caused by land use change: (<b>a</b>) NDVI trend in the sample area; (<b>b</b>) Image of the sample area acquired on 3 August 2010; (<b>c</b>) Image of the sample area acquired on 25 March 2015.</p> Full article ">Figure 8
<p>Forest reduction caused by a large-scale fire in alpine bays in Fuling District: (<b>a</b>) Mean NDVI trend in the sample area; (<b>b</b>) Image of the fire site acquired on 9 February 2008.</p> Full article ">
16 pages, 5863 KiB  
Article
An Analysis of the Evolution, Completeness and Spatial Patterns of OpenStreetMap Building Data in China
by YuanJian Tian, Qi Zhou and Xiaolin Fu
ISPRS Int. J. Geo-Inf. 2019, 8(1), 35; https://doi.org/10.3390/ijgi8010035 - 16 Jan 2019
Cited by 35 | Viewed by 5444
Abstract
OpenStreetMap (OSM) is a free map that can be created, edited, and updated by volunteers globally. The quality of OSM datasets is therefore of great concern. Extensive studies have focused on assessing the completeness (a quality measure) of OSM datasets in various countries, [...] Read more.
OpenStreetMap (OSM) is a free map that can be created, edited, and updated by volunteers globally. The quality of OSM datasets is therefore of great concern. Extensive studies have focused on assessing the completeness (a quality measure) of OSM datasets in various countries, but very few have been paid attention to investigating the OSM building dataset in China. This study aims to present an analysis of the evolution, completeness and spatial patterns of OSM building data in China across the years 2012 to 2017. This is done using two quality indicators, OSM building count and OSM building density, although a corresponding reference dataset for the whole country is not freely available. Development of OSM building counts from 2012 to 2017 is analyzed in terms of provincial- and prefecture-level divisions. Factors that may affect the development of OSM building data in China are also analyzed. A 1 × 1 km2 regular grid is overlapped onto urban areas of each prefecture-level division, and the OSM building density of each grid cell is calculated. Spatial distributions of high-density grid cells for prefecture-level divisions are analyzed. Results show that: (1) the OSM building count increases by almost 20 times from 2012 to 2017, and in most cases, economic (gross domestic product) and OSM road length are two factors that may influence the development of OSM building data in China; (2) most grid cells in urban areas do not have any building data, but two typical patterns (dispersion and aggregation) of high-density grid cells are found among prefecture-level divisions. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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<p>(<b>a</b>–<b>c</b>) Illustration of the clustering approach for analyzing spatial patterns of <span class="html-italic">high-density grid cells</span>.</p> Full article ">Figure 2
<p>OpenStreetMap (OSM) building counts in China from 2012 to 2017.</p> Full article ">Figure 3
<p>Number distribution of OSM building data for provincial-level divisions in China, from 2012 to 2017 (<b>a</b>–<b>f</b>).</p> Full article ">Figure 4
<p>Number distribution of OSM building data for prefecture-level divisions in China, from 2012 to 2017 (<b>a</b>–<b>f</b>).</p> Full article ">Figure 5
<p>Linear correlations between OSM building density and OSM building completeness for four metropolises in China (<b>a</b>–<b>d</b>).</p> Full article ">Figure 6
<p>(<b>a</b>–<b>i</b>) Variation of OSM building density from 2012 to 2017, for nine prefecture-level divisions.</p> Full article ">Figure 7
<p>(<b>a</b>–<b>b</b>) Relationship between the cluster count and maximum cluster area for prefecture-level divisions in China, in 2017. Those prefecture-level divisions that did not have any clusters are not shown.</p> Full article ">Figure 8
<p>(<b>a</b>–<b>f</b>) Density distributions of OSM building data for six typical metropolises in China, in 2017.</p> Full article ">
22 pages, 8244 KiB  
Concept Paper
Taming Disruption? Pervasive Data Analytics, Uncertainty and Policy Intervention in Disruptive Technology and its Geographic Spread
by Roger C. Brackin, Michael J. Jackson, Andrew Leyshon and Jeremy G. Morley
ISPRS Int. J. Geo-Inf. 2019, 8(1), 34; https://doi.org/10.3390/ijgi8010034 - 16 Jan 2019
Cited by 3 | Viewed by 5090
Abstract
The topic of technology development and its disruptive effects has been the subject of much debate over the last 20 years with numerous theories at both macro and micro scales offering potential models of technology progression and disruption. This paper focuses on how [...] Read more.
The topic of technology development and its disruptive effects has been the subject of much debate over the last 20 years with numerous theories at both macro and micro scales offering potential models of technology progression and disruption. This paper focuses on how theories of technology progression may be integrated and considers whether suitable indicators of this progression and any subsequent disruptive effects might be derived, based on the use of big data analytic techniques. Given the magnitude of the economic, social, and political implications of many disruptive technologies, the ability to quantify disruptive change at the earliest possible stage could deliver major returns by reducing uncertainty, assisting public policy intervention, and managing the technology transition through disruption into deployment. However, determining when this stage has been reached is problematic because small random effects in the timing, direction of development, the availability of essential supportive technologies or “platform” technologies, market response or government policy can all result in failure of a technology, its form of adoption or optimality of implementation. This paper reviews key models of technology evolution and their disruptive effect including the geographical spread of disruption. The paper then describes a use case and an experiment in disruption prediction, looking at the geographical spread of disruption using internet derived historic data. The experiment, although limited to one specific aspect of the integrated model outlined in the paper, provides an initial example of the type of analysis envisaged. This example offers a glimpse into the potential indicators and how they might be used to measure disruption hinting at what might be possible using big data approaches. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Christensen’s model of disruption (Bower and Christensen (1995)).</p> Full article ">Figure 2
<p>Mobile phone origins (derived from Mazzucato (2015) [<a href="#B8-ijgi-08-00034" class="html-bibr">8</a>]).</p> Full article ">Figure 3
<p>The technology readiness level model (NASA).</p> Full article ">Figure 4
<p>Characterisation of the hype cycle representation.</p> Full article ">Figure 5
<p>Framework for integrating different research into disruption.</p> Full article ">Figure 6
<p>Model of the nature of technology derived from Arthur (2009) [<a href="#B7-ijgi-08-00034" class="html-bibr">7</a>].</p> Full article ">Figure 7
<p>Model change over time (Arthur (2009) [<a href="#B7-ijgi-08-00034" class="html-bibr">7</a>] and Christensen (1997) [<a href="#B4-ijgi-08-00034" class="html-bibr">4</a>]).</p> Full article ">Figure 8
<p>Platform evolution cycle.</p> Full article ">Figure 9
<p>Technologies supporting the smartphone platform.</p> Full article ">Figure 10
<p>Issues affecting Uber disruption.</p> Full article ">Figure 11
<p>Store location capture experimental architecture.</p> Full article ">Figure 12
<p>Apple Store openings 2006–2017 (tabulated).</p> Full article ">Figure 13
<p>Global Apple Store openings 2006–2017 (World Mercator Projection).</p> Full article ">Figure 14
<p>Uber availability (cities per country) 2012–2016 (tabulated).</p> Full article ">Figure 15
<p>Global Uber supported cities 2012/2017 (World Mercator Projection).</p> Full article ">Figure 16
<p>Apple Store opening numbers overlaid with Uber deployment numbers. The bold lines indicate initial opening in that country for Apple Store and Uber.</p> Full article ">
21 pages, 10413 KiB  
Article
A Geospatial Application Framework for Directional Relations
by Eliseo Clementini and Giampaolo Bellizzi
ISPRS Int. J. Geo-Inf. 2019, 8(1), 33; https://doi.org/10.3390/ijgi8010033 - 15 Jan 2019
Cited by 2 | Viewed by 3134
Abstract
Geographic data analysis is based on the use of spatial relations as a means of selecting and processing geometric data associated with geographic features. Starting from 1990, topological relations have been recognized as fundamental criteria in geographic data processing, leaving out other kinds [...] Read more.
Geographic data analysis is based on the use of spatial relations as a means of selecting and processing geometric data associated with geographic features. Starting from 1990, topological relations have been recognized as fundamental criteria in geographic data processing, leaving out other kinds of spatial relations, such as directional relations. The latter ones, despite having quite an important role in geospatial applications, have been developed as theoretical models but very little implemented in systems. We refer in this paper to the 5-intersection model for expressing projective relations that can be used to implement directional relations in various frames of reference. We design an application framework in Java and use the framework for answering various categories of queries involving directions. We finally outline how to use the framework for validating the cognitive adequacy of relations with user tests. Full article
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<p>The partition of the plane into five areas by reference regions <span class="html-italic">B</span> and <span class="html-italic">C</span> (figure taken from [<a href="#B25-ijgi-08-00033" class="html-bibr">25</a>]).</p> Full article ">Figure 2
<p>Some representative relations of the 5-intersection model (figure partly taken from [<a href="#B25-ijgi-08-00033" class="html-bibr">25</a>]).</p> Full article ">Figure 3
<p>Taxonomy of frames of reference (figure taken from [<a href="#B21-ijgi-08-00033" class="html-bibr">21</a>]).</p> Full article ">Figure 4
<p>Geographic frame of reference.</p> Full article ">Figure 5
<p>Geometric frame of reference.</p> Full article ">Figure 6
<p>Finding the supermarkets in front of a given building and at a short distance.</p> Full article ">Figure 7
<p>Deictic frame of reference.</p> Full article ">Figure 8
<p>Finding buildings on the left side of the monument from the observer point of view.</p> Full article ">Figure 9
<p>The UML diagram of the deictic-allocentric frame of reference.</p> Full article ">Figure 10
<p>Architecture of the application framework.</p> Full article ">Figure 11
<p>Publishing a layer.</p> Full article ">Figure 12
<p>The main window of the application.</p> Full article ">Figure 13
<p>Buildings north of the selected one.</p> Full article ">Figure 14
<p>Boolean directional relation <span class="html-italic">north_of</span>.</p> Full article ">Figure 15
<p>Buildings where the front side can be identified.</p> Full article ">Figure 16
<p>Buildings in front of the selected building.</p> Full article ">Figure 17
<p>Setting user′s position in the deictic frame of reference.</p> Full article ">Figure 18
<p>Houses in front of the selected building from the observer′s point of view.</p> Full article ">Figure 19
<p>Test for the geographic (<b>a</b>), deictic (<b>b</b>), and geometric (<b>c</b>) frame of reference.</p> Full article ">Figure 20
<p>Results of the <span class="html-italic">north_of</span> relation with respect to the selected building: (<b>a</b>) from the application; (<b>b</b>) from the user tests.</p> Full article ">Figure 21
<p>Results of the <span class="html-italic">west_of</span> relation with respect to the selected building: (<b>a</b>) from the application; (<b>b</b>) from the user tests.</p> Full article ">Figure 22
<p>Results of the <span class="html-italic">front</span> relation with respect to the selected building: (<b>a</b>) from the application; (<b>b</b>) from the user tests.</p> Full article ">Figure 23
<p>Results of the <span class="html-italic">right_of</span> relation with respect to the selected building: (<b>a</b>) from the application; (<b>b</b>) from the user tests.</p> Full article ">Figure 24
<p>Results of the <span class="html-italic">front</span> relation with respect to the selected building: (<b>a</b>) from the application; (<b>b</b>) from the user tests.</p> Full article ">Figure 25
<p>Results of the <span class="html-italic">rightside</span> relation with respect to the selected building: (<b>a</b>) from the application; (<b>b</b>) from the user tests.</p> Full article ">
18 pages, 2840 KiB  
Article
Probabilistic Model of Random Encounter in Obstacle Space
by Zhang-Cai Yin, Hui Liu, Zhi-Jun Zhang, Zhang-Hao-Nan Jin, San-Juan Li and Jia-Qiang Xiao
ISPRS Int. J. Geo-Inf. 2019, 8(1), 32; https://doi.org/10.3390/ijgi8010032 - 15 Jan 2019
Viewed by 3276
Abstract
Based on probabilistic time-geography, the encounter between two moving objects is random. The quantitative analysis of the probability of encounter needs to consider the actual geographical environment. The existing encounter probability algorithm is based on homogeneous space, ignoring the wide range of obstacles [...] Read more.
Based on probabilistic time-geography, the encounter between two moving objects is random. The quantitative analysis of the probability of encounter needs to consider the actual geographical environment. The existing encounter probability algorithm is based on homogeneous space, ignoring the wide range of obstacles and their impact on encounter events. Based on this, this paper introduces obstacle factors, proposes encounter events that are constrained by obstacles, and constructs a model of the probability of encounters of moving objects based on the influence of obstacles on visual perception with the line-of-sight view analysis principle. In realistic obstacle space, this method provides a quantitative basis for predicting the encountering possibility of two mobile objects and the largest possible encounter location. Finally, the validity of the model is verified by experimental results. The model uses part of the Wuhan digital elevation model (DEM) data to calculate the encounter probability of two moving objects on it, and analyzes the temporal and spatial distribution characteristics of these probabilities. Full article
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<p>Topographic line-of-sight analysis: (<b>a</b>) visible; (<b>b</b>) invisible.</p> Full article ">Figure 2
<p>Terrain elevation (<b>a</b>) and viewpoint height (<b>b</b>).</p> Full article ">Figure 3
<p>Two relationships between sight and terrain.</p> Full article ">Figure 4
<p>Discretized representation of a four-dimensional sample space.</p> Full article ">Figure 5
<p>The probability calculations.</p> Full article ">Figure 6
<p>DEM.</p> Full article ">Figure 7
<p>The reachable ranges <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="sans-serif">Ω</mi> <mi>t</mi> <mi>A</mi> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="sans-serif">Ω</mi> <mi>t</mi> <mi>B</mi> </msubsup> </mrow> </semantics></math> of the two individuals A and B.</p> Full article ">Figure 8
<p>The reachable domains of two individuals at time <span class="html-italic">t</span>.</p> Full article ">Figure 9
<p>Probabilities distribution of individuals A and B.</p> Full article ">Figure 10
<p>Probability map of A meeting B (<b>a</b>) and that of B meeting A (<b>b</b>) in obstacle space.</p> Full article ">Figure 11
<p>Probability map of A meeting B (<b>a</b>) and that of B meeting A (<b>b</b>) without considering the obstacle factor.</p> Full article ">
16 pages, 3380 KiB  
Article
Spatial–Temporal Evolution and Regional Differentiation Features of Urbanization in China from 2003 to 2013
by Peiyu Zhang, Jianjun Pan, Longtao Xie, Tao Zhou, Haoran Bai and Yanxiang Zhu
ISPRS Int. J. Geo-Inf. 2019, 8(1), 31; https://doi.org/10.3390/ijgi8010031 - 15 Jan 2019
Cited by 13 | Viewed by 4407
Abstract
Quantifying the temporal and spatial patterns of impervious surfaces (IS) is important for assessing the environmental and ecological impacts of urbanization. In order to better extract IS, and to explore the divergence in urbanization in different regions, research on the regional differentiation features [...] Read more.
Quantifying the temporal and spatial patterns of impervious surfaces (IS) is important for assessing the environmental and ecological impacts of urbanization. In order to better extract IS, and to explore the divergence in urbanization in different regions, research on the regional differentiation features and regional change difference features of IS are required. To extract China’s 2013 urban impervious area, we used the 2013 night light (NTL) data and the Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index and enhanced vegetation index (EVI) temporal series data, and used three urban impervious surface extraction indexes—Human Settlements Index, Vegetation-Adjusted NTL Urban Index, and the EVI-adjusted NTL index (EANTLI)—which are recognized as the best and most widely used indexes for extracting urban impervious areas. We used the classification results of the Landsat-8 images as the benchmark data to visually compare and verify the results of the urban impervious area extracted by the three indexes. We determined that the EANTLI index better reflects the distribution of the impervious area. Therefore, we used the EANTLI index to extract the urban impervious area from 2003 to 2013 in the study area, and researched the spatial and temporal differentiation in urban IS. The results showed that China’s urban IS area was 70,179.06 km2, accounting for 0.73% of the country’s land area in 2013, compared with 20,565.24 km2 in 2003, which accounted for 0.21% of the land area, representing an increase of 0.52%. On a spatial scale, like economic development, the distribution of urban impervious surfaces was different in different regions. The overall performance of the urban IS percentage was characterized by a decreasing trend from Northwest China, Southwest China, the Middle Reaches of the Yellow River, Northeast China, the Middle Reaches of the Yangtze River, Southern Coastal China, and Northern Coastal China to Eastern Coastal China. On the provincial scale, the urban IS expansion showed considerable differences in different regions. The overall performance of the Urban IS Expansion index showed that the eastern coastal areas had higher values than the western inland areas. The cities or provinces of Beijing, Tianjin, Jiangsu, and Shanghai had the largest growth in impervious areas. Spatially and temporally quantifying the change in urban impervious areas can help to better understand the intensity of urbanization in a region. Therefore, quantifying the change in urban impervious area has an important role in the study of regional environmental and economic development, policy formulation, and the rational use of resources in both time and space. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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<p>China—the location of the study area and its provincial divisions: Northeast China (NEC), Northern Coastal China (NCC), Eastern Coastal China (ECC), Southern Coastal China (SCC), the Middle Reaches of the Yellow River (MRYLR), the Middle Reaches of the Yangtze River (MRYTR), Southwest China (SWC), and Northwest China (NWC).</p> Full article ">Figure 2
<p>The steps of the spatiotemporal patterns of urban imperious surfaces (IS).</p> Full article ">Figure 3
<p>Comparative analysis of the extraction results of the three indexes and the Landsat-8 image baseline data in eight representative cities (Beijing, Shanghai, Guangdong, Nanjing, Wuhan, Xian, Lanzhou, and Urumqi.). Urban impervious surface and distribution map extracted using (<b>a</b>) Human Settlements Index (HIS), (<b>b</b>) Vegetation-Adjusted NTL Urban Index (VANUI), and (<b>c</b>) the EVI-adjusted NTL index (EANTLI) index; (<b>d</b>) 7-, 6-, and 4-band combinations of Landsat-8 images for city and building identification.</p> Full article ">Figure 4
<p>Urban impervious area distribution map in 2013.</p> Full article ">Figure 5
<p>The change in (<b>a</b>) urban impervious surface percent (UISP) and (<b>b</b>) gross domestic product (GDP) from 2003 to 2013.</p> Full article ">Figure 6
<p>Urban impervious rate in (<b>a</b>) 2003, (<b>b</b>) 2008, and (<b>c</b>) 2013 in the eight regions.</p> Full article ">Figure 7
<p>The variety of urban impervious rate (year-on-year growth rate) from (<b>a</b>) 2003 to 2008, (<b>b</b>) 2008 to 2013 and (<b>c</b>) 2003 to 2013 in the eight regions.</p> Full article ">Figure 8
<p>Provincial city impervious area growth rate change chart from 2003 to 2013.</p> Full article ">
24 pages, 13853 KiB  
Article
Effect of DEM Interpolation Neighbourhood on Terrain Factors
by Ying Zhu, Xuejun Liu, Jing Zhao, Jianjun Cao, Xiaolei Wang and Dongliang Li
ISPRS Int. J. Geo-Inf. 2019, 8(1), 30; https://doi.org/10.3390/ijgi8010030 - 15 Jan 2019
Cited by 13 | Viewed by 4789
Abstract
Topographic factors such as slope and aspect are essential parameters in depicting the structure and morphology of a terrain surface. We study the effect of the number of points in the neighbourhood of a digital elevation model (DEM) interpolation method on mean slope, [...] Read more.
Topographic factors such as slope and aspect are essential parameters in depicting the structure and morphology of a terrain surface. We study the effect of the number of points in the neighbourhood of a digital elevation model (DEM) interpolation method on mean slope, mean aspect, and RMSEs of slope and aspect from the interpolated DEM. As the moving least squares (MLS) method can maintain the inherent properties and other characteristics of a surface, this method is chosen for DEM interpolation. Three areas containing different types of topographic features are selected for study. Simulated data from a Gauss surface is also used for comparison. First, the impact of the number of points on the DEM root mean square error (RMSE) is analysed. The DEM RMSE in the three study areas decreases gradually with the number of points in the neighbourhood. In addition, the effect of the number of points in the neighbourhood on mean slope and mean aspect was studied across varying topographies through regression analysis. The two variables respond differently to changes in terrain. However, the RMSEs of the slope and aspect in all study areas are logarithmically related to the number of points in the neighbourhood and the values decrease uniformly as the number of points in the neighbourhood increases. With more points in the neighbourhood, the RMSEs of the slope and aspect are not sensitive to topography differences and the same trends are observed for the three studied quantities. Results for the Gauss surface are similar. Finally, this study analyses the spatial distribution of slope and aspect errors. The slope error is concentrated in ridges, valleys, steep-slope areas, and ditch edges while the aspect error is concentrated in ridges, valleys, and flat regions. With more points in the neighbourhood, the number of grid cells in which the slope error is greater than 15° is gradually reduced. With similar terrain types and data sources, if the calculation efficiency is not a concern, sufficient points in the spatial autocorrelation range should be analysed in the neighbourhood to maximize the accuracy of the slope and aspect. However, selecting between 10 and 12 points in the neighbourhood is economical. Full article
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<p>Location and topography of three study areas: hill-shading maps generated from reference DEMs of 5 m for (<b>a</b>) study area 1, (<b>b</b>) study area 2, (<b>c</b>) and study area 3.</p> Full article ">Figure 2
<p>Contour maps of: (<b>a</b>) study area 1, (<b>b</b>) study area 2, and (<b>c</b>) study area 3. The contour interval is 10 m.</p> Full article ">Figure 2 Cont.
<p>Contour maps of: (<b>a</b>) study area 1, (<b>b</b>) study area 2, and (<b>c</b>) study area 3. The contour interval is 10 m.</p> Full article ">Figure 3
<p>The surface and scattered points of the simulated data. (<b>a</b>) The surface generated by formula (1) and (<b>b</b>) points randomly scattered from the surface.</p> Full article ">Figure 4
<p>Directional semivariograms of elevations; isotropic semivariograms of (<b>a</b>) study area 1, (<b>b</b>) study area 2, (<b>c</b>) study area 3, and (<b>d</b>) simulated data; anisotropic semivariogram (0°) of (a1) study area 1, (<b>b1</b>) study area 2, (<b>c1</b>) study area 3, and (<b>d1</b>) simulated data; anisotropic semivariogram (45°) of (<b>a2</b>) study area 1, (<b>b2</b>) study area 2, (<b>c2</b>) study area 3, and (<b>d2</b>) simulated data; anisotropic semivariogram (90°) of (<b>a3</b>) study area 1, (<b>b3</b>) study area 2, (<b>c3</b>) study area 3, and (<b>d3</b>) simulated data; anisotropic semivariogram (135°) of (<b>a4</b>) study area 1, (<b>b4</b>) study area 2, (<b>c4</b>) study area 3, and (<b>d4</b>) simulated data.</p> Full article ">Figure 4 Cont.
<p>Directional semivariograms of elevations; isotropic semivariograms of (<b>a</b>) study area 1, (<b>b</b>) study area 2, (<b>c</b>) study area 3, and (<b>d</b>) simulated data; anisotropic semivariogram (0°) of (a1) study area 1, (<b>b1</b>) study area 2, (<b>c1</b>) study area 3, and (<b>d1</b>) simulated data; anisotropic semivariogram (45°) of (<b>a2</b>) study area 1, (<b>b2</b>) study area 2, (<b>c2</b>) study area 3, and (<b>d2</b>) simulated data; anisotropic semivariogram (90°) of (<b>a3</b>) study area 1, (<b>b3</b>) study area 2, (<b>c3</b>) study area 3, and (<b>d3</b>) simulated data; anisotropic semivariogram (135°) of (<b>a4</b>) study area 1, (<b>b4</b>) study area 2, (<b>c4</b>) study area 3, and (<b>d4</b>) simulated data.</p> Full article ">Figure 5
<p>Plot of DEM RMSEs of three study areas and simulated data by number of points in the neighbourhood.</p> Full article ">Figure 6
<p>Plot of mean slope with number of points in the neighbourhood in study areas 1–3 and simulated data.</p> Full article ">Figure 7
<p>Plot of mean aspect with number of points in the neighbourhood in study areas 1–3 and simulated data.</p> Full article ">Figure 8
<p>Histogram of slope grade with number of grid cells in study areas 1–3. There are 5° per grade. The columns from left to right in each group respectively correspond to the number of grid cells of each slope grade in slope<sub>i</sub> (i = 5, 6, 7, …, 20). Histograms of (<b>a</b>) study area 1, (<b>b</b>) study area 2, and (<b>c</b>) study area 3.</p> Full article ">Figure 8 Cont.
<p>Histogram of slope grade with number of grid cells in study areas 1–3. There are 5° per grade. The columns from left to right in each group respectively correspond to the number of grid cells of each slope grade in slope<sub>i</sub> (i = 5, 6, 7, …, 20). Histograms of (<b>a</b>) study area 1, (<b>b</b>) study area 2, and (<b>c</b>) study area 3.</p> Full article ">Figure 9
<p>Plot of the slope RMSE with number of points in the neighbourhood in study areas 1–3 and simulated data.</p> Full article ">Figure 10
<p>Plot of the aspect RMSE with number of points in the neighbourhood in study areas 1–3 and simulated data.</p> Full article ">Figure 11
<p>Spatial distribution of the slope error (the blue parts in (<b>a</b>–<b>c</b>) respectively indicate all grid cells of study area 1, study area 2, and study area 3 with relative errors greater than 100%; the blue parts in (<b>d</b>–<b>f</b>) respectively indicate all grid cells of study area 1, study area 2, and study area 3 with absolute slope errors greater than 15°).</p> Full article ">Figure 12
<p>Spatial distributions of aspect errors ((<b>a</b>–<b>c</b>) respectively represent the spatial distributions of aspect errors of study area 1, study area 2, and study area 3).</p> Full article ">Figure 13
<p>Hill-shading maps generated from DEMs for (<b>a</b>) DEM<sub>5</sub> of study area 1, (<b>b</b>) DEM<sub>5</sub> of study area 2, (<b>c</b>) DEM<sub>5</sub> of study area 3, (<b>d</b>) DEM<sub>20</sub> of study area 1, (<b>e</b>) DEM<sub>20</sub> of study area 2, and (<b>f</b>) DEM<sub>20</sub> of study area 3.</p> Full article ">
23 pages, 6670 KiB  
Article
Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation
by Tengfei Yang, Jibo Xie, Guoqing Li, Naixia Mou, Zhenyu Li, Chuanzhao Tian and Jing Zhao
ISPRS Int. J. Geo-Inf. 2019, 8(1), 29; https://doi.org/10.3390/ijgi8010029 - 15 Jan 2019
Cited by 35 | Viewed by 7782
Abstract
Social media contains a lot of geographic information and has been one of the more important data sources for hazard mitigation. Compared with the traditional means of disaster-related geographic information collection methods, social media has the characteristics of real-time information provision and low [...] Read more.
Social media contains a lot of geographic information and has been one of the more important data sources for hazard mitigation. Compared with the traditional means of disaster-related geographic information collection methods, social media has the characteristics of real-time information provision and low cost. Due to the development of big data mining technologies, it is now easier to extract useful disaster-related geographic information from social media big data. Additionally, many researchers have used related technology to study social media for disaster mitigation. However, few researchers have considered the extraction of public emotions (especially fine-grained emotions) as an attribute of disaster-related geographic information to aid in disaster mitigation. Combined with the powerful spatio-temporal analysis capabilities of geographical information systems (GISs), the public emotional information contained in social media could help us to understand disasters in more detail than can be obtained from traditional methods. However, the social media data is quite complex and fragmented, both in terms of format and semantics, especially for Chinese social media. Therefore, a more efficient algorithm is needed. In this paper, we consider the earthquake that happened in Ya’an, China in 2013 as a case study and introduce the deep learning method to extract fine-grained public emotional information from Chinese social media big data to assist in disaster analysis. By combining this with other geographic information data (such population density distribution data, POI (point of interest) data, etc.), we can further assist in the assessment of affected populations, explore emotional movement law, and optimize disaster mitigation strategies. Full article
(This article belongs to the Special Issue Big Data Computing for Geospatial Applications)
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<p>Framework of the automatic emotion classification and disaster analysis.</p> Full article ">Figure 2
<p>The study area of the 2013 Ya’an earthquake that was used in this paper.</p> Full article ">Figure 3
<p>Structure of the text feature vector.</p> Full article ">Figure 4
<p>Structure of the convolutional neural network (CNN) model used in the paper.</p> Full article ">Figure 5
<p>Emotional distribution characteristics of the affected population. The figure (<b>a</b>), (<b>b</b>), (<b>c</b>), (<b>d</b>) and (<b>e</b>) describe the distribution of emotions in different time periods within 72 hours after the disaster. The figure (<b>f</b>) shows the distribution of emotions over 72 hours. Among them, each of red circle 1, red circle 2, and red circle 3 in the figures represent the same area. The blue circle 1 in (<b>b</b>) shows that compared with (<b>a</b>), new negative emotions emerged in same area.</p> Full article ">Figure 6
<p>Changes in different emotion categories in data volume for different periods of time.</p> Full article ">Figure 7
<p>Changes of the crowd amount in each small time period.</p> Full article ">Figure 8
<p>The change characteristics of public spatio-temporal trajectory. This sequence diagram describes how the crowd moved in different small time periods after the earthquake. Among them, the figure (<b>a</b>) shows the trajectories of public change in 10 minutes after the earthquake. Three clusters were formed in this period. The figure (<b>b</b>) shows the location relationship between each cluster and shelters in the second ten minutes. The figure (<b>c</b>), (<b>d</b>) and (<b>e</b>) shows that all small clusters formed a large cluster over time and it had the largest population between 08:40 and 09:00 as in figure (<b>d</b>). The figure (<b>f</b>) and (<b>g</b>) shows crowd was gradually dissipating and leaving the shelter. From the whole process of analysis, we determined that: (1) When the earthquake happened, people rushed to the shelters in a very short time period. However, were these shelters reasonably laid out? We saw that some shelters did not contain many people, or even had no people. Therefore, the analysis results could be used as a reference for the rational layout of shelters. (2) The characteristics of crowd gathering and evacuation could be used as an effective reference to aid disaster reduction departments in dealing with future emergencies.</p> Full article ">Figure 9
<p>Sequence diagram of positive emotion (the words in the text box are the hot words related to this emotion in the corresponding time period).</p> Full article ">Figure 10
<p>Sequence diagram of anxiety.</p> Full article ">Figure 11
<p>Sequence diagram of anger.</p> Full article ">Figure 12
<p>Sequence diagram of sadness.</p> Full article ">Figure 13
<p>Sequence diagram of fear.</p> Full article ">Figure 14
<p>Classification accuracy of different emotions.</p> Full article ">Figure 15
<p>Comparison of the number of pieces of address information with different accuracy in each city.</p> Full article ">
16 pages, 8691 KiB  
Article
Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping based on a Modified Two-Branch Convolutional Neural Network
by Quanlong Feng, Dehai Zhu, Jianyu Yang and Baoguo Li
ISPRS Int. J. Geo-Inf. 2019, 8(1), 28; https://doi.org/10.3390/ijgi8010028 - 14 Jan 2019
Cited by 82 | Viewed by 6995
Abstract
Accurate urban land-use mapping is a challenging task in the remote-sensing field. With the availability of diverse remote sensors, synthetic use and integration of multisource data provides an opportunity for improving urban land-use classification accuracy. Neural networks for Deep Learning have achieved very [...] Read more.
Accurate urban land-use mapping is a challenging task in the remote-sensing field. With the availability of diverse remote sensors, synthetic use and integration of multisource data provides an opportunity for improving urban land-use classification accuracy. Neural networks for Deep Learning have achieved very promising results in computer-vision tasks, such as image classification and object detection. However, the problem of designing an effective deep-learning model for the fusion of multisource remote-sensing data still remains. To tackle this issue, this paper proposes a modified two-branch convolutional neural network for the adaptive fusion of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. Specifically, the proposed model consists of a HSI branch and a LiDAR branch, sharing the same network structure to reduce the time cost of network design. A residual block is utilized in each branch to extract hierarchical, parallel, and multiscale features. An adaptive-feature fusion module is proposed to integrate HSI and LiDAR features in a more reasonable and natural way (based on “Squeeze-and-Excitation Networks”). Experiments indicate that the proposed two-branch network shows good performance, with an overall accuracy of almost 92%. Compared with single-source data, the introduction of multisource data improves accuracy by at least 8%. The adaptive fusion model can also increase classification accuracy by more than 3% when compared with the feature-stacking method (simple concatenation). The results demonstrate that the proposed network can effectively extract and fuse features for a better urban land-use mapping accuracy. Full article
(This article belongs to the Special Issue Multi-Source Geoinformation Fusion)
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<p>Datasets, training, and testing samples used in this study.</p> Full article ">Figure 1 Cont.
<p>Datasets, training, and testing samples used in this study.</p> Full article ">Figure 2
<p>Architecture of the proposed two-branch convolutional neural network.</p> Full article ">Figure 3
<p>Architecture of the proposed hyperspectral imagery (HSI) branch.</p> Full article ">Figure 4
<p>Architecture of Residual block-A and Residual block-B in the HSI branch. O: Output; C: Concatenate; +: Sum.</p> Full article ">Figure 5
<p>Patch size k vs. overall accuracy.</p> Full article ">Figure 6
<p>Structure of the adaptive feature-fusion module. C: Concatenate; ×: pointwise production.</p> Full article ">Figure 7
<p>Classification maps for (<b>a</b>) HSI branch only; (<b>b</b>) LiDAR branch only; (<b>c</b>) proposed two-branch CNN.</p> Full article ">
31 pages, 10273 KiB  
Article
Exploiting the Potential of Integrated Public Building Data: Energy Performance Assessment of the Building Stock in a Case Study in Northern Italy
by Alice Pasquinelli, Giorgio Agugiaro, Lavinia Chiara Tagliabue, Marco Scaioni and Franco Guzzetti
ISPRS Int. J. Geo-Inf. 2019, 8(1), 27; https://doi.org/10.3390/ijgi8010027 - 14 Jan 2019
Cited by 14 | Viewed by 4020
Abstract
Smart management of urban built environment relies on the availability of data supporting sound policy making and guiding city renovation processes toward more sustainable and performant models. Nevertheless, public managers are unlikely to have comprehensive information on the existing building stock. In addition, [...] Read more.
Smart management of urban built environment relies on the availability of data supporting sound policy making and guiding city renovation processes toward more sustainable and performant models. Nevertheless, public managers are unlikely to have comprehensive information on the existing building stock. In addition, tools providing effective insights on potential costs and benefits of retrofit strategies at city/district scale are hardly available. This article describes how data related to existing buildings may be effectively combined together into a so-called Building Information System, and discusses the advantages and shortcomings related to this process. At the same time, the implementation on a real case study in northern Italy demonstrates how the effort due to data harmonization and integration is able to foster applications to support policy makers in the management of the built environment and in the definition of urban sustainability strategies. Building data were harmonized according to the requirements of the international open standard CityGML, therefore facilitating the exchange of building information. The whole project was carried out while considering the characteristics of data sources that are available for each public body in Italy and, as a consequence, it may be replicated to other Italian municipalities. Full article
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<p>Example of a single building in the Topographic Database (TDB) (on the <b>left</b>), subdivided in four properties in the cadastre (on the <b>right</b>). Image source: authors.</p> Full article ">Figure 2
<p>Position of the province of Brescia in Italy (on the <b>left</b>). The municipality of Gavardo within the Sabbia Valley (on the <b>right</b>), which was selected as case study area. Image source: authors.</p> Full article ">Figure 3
<p>The study area in the municipality of Gavardo. Image source: authors.</p> Full article ">Figure 4
<p>Schematic view of the data manipulation process implemented in Feature Manipulation Engine 2017 (FME). Image source: authors.</p> Full article ">Figure 5
<p>Three-dimensional (3D) view of the Gavardo city model published on the web using the free virtual globe library Cesium. Image source: authors.</p> Full article ">Figure 6
<p>Buildings classified according to the measured gas consumption and published in the free virtual globe library Cesium. Image source: authors.</p> Full article ">Figure 7
<p>Heating energy demand computed with data package DP1. Image source: authors.</p> Full article ">Figure 8
<p>Heating energy demand computed with data package DP2. Image source: authors.</p> Full article ">Figure 9
<p>Comparison of results obtained from packages DP1 and DP2 with respect to the energy performance certificates (EPCs) of 18 buildings in the study area. Image source: authors.</p> Full article ">Figure 10
<p>Comparison of results that were obtained from packages DP1 and DP2 with respect to consumptions of 77 buildings in the study area. Image source: authors.</p> Full article ">Figure 11
<p>Comparison of retrofitting scenarios. Please note that the use of the line chart is intended only to facilitate the comparison, but building values do not correlate to one other. Image source: authors.</p> Full article ">Figure 12
<p>Estimated energy consumption for the retrofitting scenario “Wall insulation”. Image source: authors.</p> Full article ">Figure 13
<p>Estimated energy consumption for the retrofitting scenario “Roof insulation”. Image source: authors.</p> Full article ">Figure 14
<p>Estimated energy consumption for the retrofitting scenario “Windows improvement”. Image source: authors.</p> Full article ">Figure 15
<p>Estimated energy consumption for the retrofitting scenario “Total retrofitting”. Image source: authors.</p> Full article ">
14 pages, 3363 KiB  
Article
Adaptive Non-Negative Geographically Weighted Regression for Population Density Estimation Based on Nighttime Light
by Hone-Jay Chu, Chen-Han Yang and Chelsea C. Chou
ISPRS Int. J. Geo-Inf. 2019, 8(1), 26; https://doi.org/10.3390/ijgi8010026 - 12 Jan 2019
Cited by 31 | Viewed by 5750
Abstract
Nighttime light imagery provides a perspective for studying urbanization and socioeconomic changes. Traditional global regression models have been applied to explore the nonspatial relationship between nighttime lights and population density. In this study, geographically weighted regression (GWR) identifies the spatially varying relationships between [...] Read more.
Nighttime light imagery provides a perspective for studying urbanization and socioeconomic changes. Traditional global regression models have been applied to explore the nonspatial relationship between nighttime lights and population density. In this study, geographically weighted regression (GWR) identifies the spatially varying relationships between population density and nighttime lights in mainland China. However, the rural population does not have a strong relationship with remote-sensing spectral features. The rural population estimation using nighttime light data alone easily identifies meaningless negative population density in the rural area. This study proposes an adaptive non-negative GWR (ANNGWR) to explore the spatial pattern of population density by using nonnegative constraints with an adaptive bandwidth of kernel. The ANNGWR solves the negative value of population density and serious overestimation of the western boundary. The result shows that the ANNGWR provides the best goodness-of-fit compared with linear regression and original GWR. This study applies Moran’s I index to prove that the ANNGWR substantially decreases the spatial autocorrelation of the model residual. The model offers a robust and effective approach for estimating the spatial patterns of regional population density solely on the basis of nighttime light imagery. Full article
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<p>(<b>a</b>) County center locations and (<b>b</b>) values of observed population data in mainland China at the county level in 2007.</p> Full article ">Figure 2
<p>Flowchart of ANNGWR in the study.</p> Full article ">Figure 3
<p>Population density estimation based on (<b>a</b>) OLS, (<b>b</b>) GWR, (<b>c</b>) NNGWR, and (<b>d</b>) ANNGWR in 2007 (black color: negative population density).</p> Full article ">Figure 4
<p>Spatial maps of local Moran’s I coefficient based on (<b>a</b>) OLS, (<b>b</b>) GWR, (<b>c</b>) NNGWR, and (<b>d</b>) ANNGWR in 2007.</p> Full article ">Figure 5
<p>Simulation and observation population density plots from (<b>a</b>) OLS, (<b>b</b>) GWR, (<b>c</b>) NNGWR, and (<b>d</b>) ANNGWR in 2007.</p> Full article ">Figure 6
<p>Bandwidth adjustment iteratively in ANNGWR, e.g., the bandwidths are (<b>a</b>) 0.01, (<b>b</b>) 0.05, (<b>c</b>) 0.1, and (<b>d</b>) 0.2.</p> Full article ">Figure 7
<p>Spatial maps of (<b>a</b>) intercept and (<b>b</b>) slope coefficients in ANNGWR in 2007.</p> Full article ">Figure 8
<p>Estimated population density maps in (<b>a</b>) 2004, (<b>b</b>) 2007, (<b>c</b>) 2010, and (<b>d</b>) 2013 using ANNGWR.</p> Full article ">
22 pages, 7552 KiB  
Article
Application of UAV Photogrammetry in Displacement Measurement of the Soil Nail Walls Using Local Features and CPDA Method
by Farid Esmaeili, Hamid Ebadi, Mohammad Saadatseresht and Farzin Kalantary
ISPRS Int. J. Geo-Inf. 2019, 8(1), 25; https://doi.org/10.3390/ijgi8010025 - 11 Jan 2019
Cited by 9 | Viewed by 4467
Abstract
The high cost of land across urban areas has made the excavation a typical practice to construct multiple underground stories. Various methods have been used to restrain the excavated walls and keep them from a possible collapse, including nailing and anchorage. The excavated [...] Read more.
The high cost of land across urban areas has made the excavation a typical practice to construct multiple underground stories. Various methods have been used to restrain the excavated walls and keep them from a possible collapse, including nailing and anchorage. The excavated wall monitoring, especially during the drilling and restraining operations, is necessary for preventing the risk of such incidents as an excavated wall collapse. In the present research, an unmanned aerial vehicle (UAV) photogrammetry-based algorithm was proposed for accurate, fast and low-cost monitoring of excavated walls. Different stages of the proposed methodology included design of the UAV photogrammetry network for optimal imaging, local feature extraction from the acquired images, a special optimal matching method and finally, displacement estimation through a combined adjustment method. Results of implementations showed that, using the proposed methodology, one can achieve a precision of ±7 mm in positioning local features on the excavated walls. Moreover, the wall displacement could be measured at an accuracy of ±1 cm. Having high flexibility, easy implementation, low cost and fast pace; the proposed methodology provides an appropriate alternative to micro-geodesic procedures and the use of instrumentations for excavated wall displacement monitoring. Full article
(This article belongs to the Special Issue Applications and Potential of UAV Photogrammetric Survey)
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Full article ">Figure 1
<p>A qualitative comparison between close-range photogrammetric method and other methods in the measuring displacement of large-scale structures.</p> Full article ">Figure 2
<p>Collapse of a nail-restrained excavated wall in Tehran, Iran: (<b>a</b>) crack initiation on the wall, (<b>b</b>) start of the excavated wall collapse and (<b>c</b>) collapse of the vehicles and constructions on top of the excavated wall along with the wall into the excavated pit.</p> Full article ">Figure 3
<p>The flow of implementing a UAV photogrammetry system for displacement measurement of a structure.</p> Full article ">Figure 4
<p>An example of wall nailing and the metal plate attached to the nails.</p> Full article ">Figure 5
<p>Metal plates on the nails, as extracted using the MSER algorithm. Further seen is the ellipse fitted to the pixels within the region and the centre of the ellipse as a highly stable point feature.</p> Full article ">Figure 6
<p>An example of initial correspondence matching between two images, with false correspondences omitted.</p> Full article ">Figure 7
<p>Three examples of the homographic images projected on the reference image.</p> Full article ">Figure 8
<p>Final corresponding points extracted from images.</p> Full article ">Figure 9
<p>Introduction and location of the studied project.</p> Full article ">Figure 10
<p>The excavation area and its dimensions.</p> Full article ">Figure 11
<p>The Phantom 3 Pro when imaging the Seoul excavation pit, along with the drone pilot.</p> Full article ">Figure 12
<p>“Sony EXMOR 1/2.3”—4 K” camera mounted on phantom 3 professional.</p> Full article ">Figure 13
<p>Sample observations captured using the Phantom 3 Pro for displacement monitoring of the excavated wall.</p> Full article ">Figure 14
<p>Sample results of local feature extraction from the UAV-captured images from the Seoul excavation project.</p> Full article ">Figure 15
<p>Example of the results of correspondence matching on the UAV-captured observations over the Seoul excavation project.</p> Full article ">Figure 16
<p>Generated three-dimensional coordinates of the points and some of the cameras (not in real scale).</p> Full article ">Figure 17
<p>Examples of the scale bars used across the project area.</p> Full article ">
21 pages, 6202 KiB  
Article
A Comparison of Standard Modeling Techniques Using Digital Aerial Imagery with National Elevation Datasets and Airborne LiDAR to Predict Size and Density Forest Metrics in the Sapphire Mountains MT, USA
by Robert Ahl, John Hogland and Steve Brown
ISPRS Int. J. Geo-Inf. 2019, 8(1), 24; https://doi.org/10.3390/ijgi8010024 - 11 Jan 2019
Cited by 5 | Viewed by 3350
Abstract
In recent years airborne Light Detection and Ranging (LiDAR) technology has received a great deal of attention. Using airborne LiDAR, analysts have successfully related height measurements to forest characteristics such as tree size, basal area, and number of trees. Similarly, National Agricultural Imagery [...] Read more.
In recent years airborne Light Detection and Ranging (LiDAR) technology has received a great deal of attention. Using airborne LiDAR, analysts have successfully related height measurements to forest characteristics such as tree size, basal area, and number of trees. Similarly, National Agricultural Imagery Program (NAIP) digital aerial imagery in combination with elevation datasets such as the National Elevation Dataset (NED) have been used to estimate similar forest characteristics. Few comparisons, however, have been made between using airborne LiDAR, NAIP, and NED to estimate forest characteristics. In this study we compare airborne LiDAR, NAIP, and NAIP assisted NED based models of forest characteristics commonly used within forest management at the spatial scale of field plots and forest stands. Our findings suggest that there is a high degree of similarity in model fit and estimated values when using LiDAR, NAIP, and NAIP assisted NED predictor variables. Full article
(This article belongs to the Special Issue Geographic Information Science in Forestry)
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<p>Daly-Gold Study Area in the Sapphire Mountains of the Bitterroot National Forest in western Montana, USA is shown. The yellow linear features describe the National Forest Systems administrative boundary and the land within it, and the yellow point features illustrate the location of plots and associated field measurements used in this study.</p> Full article ">Figure 2
<p>Composition and structure of common stand exam plot design, with outer circle (orange) representing a 0.04 hectare plot for trees greater than or equal to 12.7 cm diameter at breast height (radius = 11.4 m) and inner circle (yellow) representing a 0.0013 ha plot for trees less than 12.7 cm DBH (radius = 2 m). The outer square represents the area summarized for the NAIP imagery and LiDAR data.</p> Full article ">Figure 3
<p>Flow diagram of the data sources, processing steps, modeling, and comparisons made within the study. NAIP, LiDAR, and TOPO data were converted to texture, height, and elevation metrics and transformed to spatial surfaces using principal component analysis (PCA). PCA surface values were then related to field data based on plot locations (Overlay) and were used to model basal area weighted diameter (BAWD), quadratic mean diameter (QMD), basal area per hectare (BAH) and trees per hectare (TPH) relationships. Modeled relationships were then used to create raster surfaces that were compared at the spatial resolution of the plot and stand.</p> Full article ">Figure 4
<p>Daly-Gold Study Area lifeform classification with randomly selected forest polygons highlighted in black. Summarized metric predictions were compared in these polygons.</p> Full article ">Figure 5
<p>Distribution of basal area weighted diameter (BAWD), quadratic mean diameter (QMD), basal area per hectare (BAH), and trees per hectare (TPH) collected on 60 plots within the study area.</p> Full article ">Figure 6
<p>Principal component graph, where LiDAR is solid black, NAIP is dashed, and TOPO is dotted. The solid horizontal line indicates the minimum 95% threshold of the cumulative variation that is explained by the number of principal components.</p> Full article ">Figure 7
<p>Illustration of TOPO, NAIP, and LiDAR principal component raster datasets. Note that only the first three components are displayed by the red-green-blue-color composite. The proportion of correlation explained (λ) by those components is display above each raster dataset.</p> Full article ">Figure 8
<p>Linear relationship between basal area weighted diameter (BAWD), quadratic mean diameter (QMD), basal area per hectare (BAH), and trees per hectare (TPH) estimates derived from NAIP and LiDAR models (<a href="#ijgi-08-00024-t001" class="html-table">Table 1</a>). Observed versus predicted values for NAIP and LiDAR based BAWD, QMD, BAH, and TPH are given in <a href="#ijgi-08-00024-f0A2" class="html-fig">Figure A2</a>.</p> Full article ">Figure 9
<p>Variation in out of bag root mean squared error (RMSE) for 10 basal area weighted diameter Random Forest models. Results for quadratic mean diameter, basal area per hectare, and trees per hectare are given in <a href="#ijgi-08-00024-f0A3" class="html-fig">Figure A3</a>.</p> Full article ">Figure A1
<p>Example of minor differences in basal area weighted diameter (BAWD with field measured min: 0.00 cm, and max: 58.62 cm), quadratic mean diameter (QMD, with field measured min: 1.27 cm, and max: 53.34 cm), basal area per hectare (BAH with field measured min: 0.00 cm, and max: 45.22 cm), and trees per hectare (TPH with field measured min: 0.00, and max: 41,439.54) estimated values for NAIP, LiDAR and TOPO based models. Additional summary statistics are given in <a href="#ijgi-08-00024-t0A1" class="html-table">Table A1</a>.</p> Full article ">Figure A2
<p>Observed verse predicted values for NAIP (left) and LiDAR (right) based, basal area weighted diameter (BAWD), quadratic mean diameter (QMD), basal area per hectare (BAH), trees per hectare (TPH), best fitting Random Forest models.</p> Full article ">Figure A3
<p>Variation in out of bag root mean squared error (RMSE) for 10 quadratic mean diameter (QMD), basal area per hectare (BAH), and trees per hectare (TPH) Random Forest models.</p> Full article ">
20 pages, 5995 KiB  
Article
Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression
by Xinxin Zhang, Bo Huang and Shunzhi Zhu
ISPRS Int. J. Geo-Inf. 2019, 8(1), 23; https://doi.org/10.3390/ijgi8010023 - 11 Jan 2019
Cited by 39 | Viewed by 5473
Abstract
Taxicabs play an important role in urban transit systems, and their ridership is significantly influenced by the urban built environment. The intricate relationship between taxi ridership and the urban environment has been explored using either conventional ordinary least squares (OLS) regression or geographically [...] Read more.
Taxicabs play an important role in urban transit systems, and their ridership is significantly influenced by the urban built environment. The intricate relationship between taxi ridership and the urban environment has been explored using either conventional ordinary least squares (OLS) regression or geographically weighted regression (GWR). However, time constitutes a significant dimension, particularly when analyzing spatiotemporal hourly taxi ridership, which is not effectively incorporated into conventional models. In this study, the geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal heterogeneity of hourly taxi ridership, and visualize the spatial and temporal coefficient variations. To test the performance of the GTWR model, an empirical study was implemented for Xiamen city in China using a set of weekday taxi pickup point data. Using point-of-interest (POI) data, hourly taxi ridership was analyzed by incorporating it to various spatially urban environment variables based on a 500 × 500 m grid unit. Compared to the OLS and GWR, the GTWR model obtained the best performance, both in terms of model fit and explanatory accuracy. Moreover, the urban environment was revealed to have a significant impact on taxi ridership. Road density was found to decrease the number of taxi trips in particular places, and the density of bus stops competed with taxi ridership over time. The GTWR modelling provides valuable insights for investigating taxi ridership variation as a function of spatiotemporal urban environment variables, thereby facilitating an optimal allocation of taxi resources and transportation planning. Full article
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<p>Flowchart of the proposed method for estimating hourly taxi trips on a 500 × 500 m<sup>2</sup> grid unit.</p> Full article ">Figure 2
<p>Location and grid-based segmentation of Xiamen Island.</p> Full article ">Figure 3
<p>Hourly average taxi ridership of Xiamen Island.</p> Full article ">Figure 4
<p>Spatial distribution of taxi ridership at different periods. (<b>a</b>) Whole day; (<b>b</b>) morning peak; (<b>c</b>) afternoon peak; and (<b>d</b>) evening peak.</p> Full article ">Figure 5
<p>Spatial distribution of different explanatory variables. (<b>a</b>) Residential; (<b>b</b>) commercial; (<b>c</b>) employment; (<b>d</b>) public services; (<b>e</b>) hotels; (<b>f</b>) attractions; (<b>g</b>) bus stops; and (<b>h</b>) road density.</p> Full article ">Figure 5 Cont.
<p>Spatial distribution of different explanatory variables. (<b>a</b>) Residential; (<b>b</b>) commercial; (<b>c</b>) employment; (<b>d</b>) public services; (<b>e</b>) hotels; (<b>f</b>) attractions; (<b>g</b>) bus stops; and (<b>h</b>) road density.</p> Full article ">Figure 6
<p>The parameter selection for the GTWR model.</p> Full article ">Figure 7
<p>Scatter plots for model fitting and cross validation. (<b>a</b>) OLS; (<b>b</b>) GWR; and (<b>c</b>) GTWR.</p> Full article ">Figure 8
<p>Spatial distribution of the coefficients of ‘residential density’. (<b>a</b>) GWR; and (<b>b</b>) GTWR. Region A has the highest density of residential buildings. Regions B and C are two newly developed zones.</p> Full article ">Figure 9
<p>Spatial distribution of the coefficients of ‘attraction density’ (quantified by natural breaks style). (<b>a</b>) GWR; and (<b>b</b>) GTWR. Region A has negative coefficients of ‘attraction density’; Regions B and C have positive coefficients of ‘attraction density’.</p> Full article ">Figure 10
<p>Temporal variations in the predicted and observed values.</p> Full article ">Figure 11
<p>Spatial and temporal distribution of the average coefficient for the “road length” variable at three peaks. (<b>a</b>) Moring peak; (<b>b</b>) afternoon peak; and (<b>c</b>) evening peak. Region A represents high density of road; Region B represents railway station.</p> Full article ">Figure 12
<p>Location of four representative regions.</p> Full article ">Figure 13
<p>Temporal variation in taxi ridership and coefficients of explanatory variables at four selected regions based on the GTWR model. (<b>a</b>) Railway station; (<b>b</b>) high density of the residential buildings region; (<b>c</b>) high density of the region comprising places of employment; and (<b>d</b>) high density at attractions region.</p> Full article ">
16 pages, 7477 KiB  
Article
Investigating the Utility Potential of Low-Cost Unmanned Aerial Vehicles in the Temporal Monitoring of a Landfill
by Abdullah Harun Incekara, Ahmet Delen, Dursun Zafer Seker and Cigdem Goksel
ISPRS Int. J. Geo-Inf. 2019, 8(1), 22; https://doi.org/10.3390/ijgi8010022 - 11 Jan 2019
Cited by 26 | Viewed by 4497
Abstract
The collection of solid waste is a challenging issue, especially in highly urbanized areas. In developing countries, landfilling is currently the preferred method for disposing of solid waste, but each landfill has a limited lifecycle. Therefore, changes in the amount of stored waste [...] Read more.
The collection of solid waste is a challenging issue, especially in highly urbanized areas. In developing countries, landfilling is currently the preferred method for disposing of solid waste, but each landfill has a limited lifecycle. Therefore, changes in the amount of stored waste should be monitored for the sustainable management of such areas. In this study, volumetric changes in a landfill were examined using a low-cost unmanned aerial vehicle (UAV). Aerial photographs obtained from five different flights, covering approximately two years, were used in the volume calculations. Values representing the amount of remaining space between the solid waste and a reference plane were determined using digital elevation models, which were produced based on the structure from motion (SfM) approach. The obtained results and potential of UAVs in the photogrammetric survey of a landfill were further evaluated and interpreted by considering other possible techniques, ongoing progress, and the information existing in an environmental impact assessment report. As a result of the study, it was proved that SfM carried out using a low-cost UAV has a high potential for use in the reconstruction of a landfill. Outcomes were obtained over a short period, without the need for direct contact with the solid waste, making the UAV preferable for use in planning and decision-making studies. Full article
(This article belongs to the Special Issue Applications and Potential of UAV Photogrammetric Survey)
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<p>Study area.</p> Full article ">Figure 2
<p>Applied methodology involving integration of ground control points (GCPs) with photographs and production of digital elevation model (DEM).</p> Full article ">Figure 3
<p>The unmanned aerial vehicle (UAV) employed (<b>left</b>) and the distribution of GCPs on the road surrounding the landfill (<b>right</b>).</p> Full article ">Figure 4
<p>(<b>a</b>) Outer boundary on the tiled model; (<b>b</b>) outer boundary on the corresponding DEM.</p> Full article ">Figure 5
<p>(<b>a</b>) Clipped tiled model which includes only the storage area; (<b>b</b>) clipped DEM corresponding to the clipped tiled model.</p> Full article ">Figure 6
<p>DEMs according to the chronology of data acquisition dates.</p> Full article ">Figure 6 Cont.
<p>DEMs according to the chronology of data acquisition dates.</p> Full article ">Figure 7
<p>(<b>a</b>) Theory of volumetric calculations for the remaining volume; (<b>b</b>) 3D visualization of the volumetric calculation.</p> Full article ">Figure 8
<p>Obtaining per-day volume value by using statistical values in the comparison.</p> Full article ">Figure 9
<p>Total population change for the four districts from 2008 to 2017, according to different sources.</p> Full article ">
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