Search Results (53)

Surveying and spatial science are experiencing a paradigm shift from traditional data outputs to more immersive and interactive formats, driven by the rise in Virtual Reality (VR). This study addresses the challenge of transforming UAV (Unmanned Aerial Vehicle)-acquired photogrammetry data into VR-compatible surfaces while preserving the accuracy and quality crucial to professional surveying. The study leverages Blender, an open-source 3D creation tool, to develop a procedural guide for creating VR-ready models from high-quality survey data. The case study focuses on silos located in Yelarbon, Southeast Queensland, Australia. UAV mapping is utilised to gather the data necessary for 3D modelling with a few minor alterations in the photo capturing angle and processing. Key findings reveal that while Blender excels as a visualisation tool, it struggles with geospatial precision, particularly when handling large numbers coming from coordinate systems, leading to rounding errors seen within the VR model. Blender’s strength lies in creating immersive experiences for public engagement but is constrained by its lack of capability to hold survey metadata, hindering its applicability for professional survey-grade outputs. The results highlight the need for further development into possible Blender plugins that integrate geospatial accuracy with VR outputs. This study underscores the potential of VR to enhance how survey data are visualised, offering opportunities for future innovations in both the technical and creative aspects of the surveying profession. Full article

Efforts to establish Pakistan’s National Spatial Data Infrastructure (NSDI) have been underway for the past 15 years, and therefore it is necessary to gauge the current progress to channelize efforts into areas that need improvement. This article assessed Pakistan’s NSDI implementation efforts through well-established approaches, including the SDI readiness model, organizational aspects, and state of play. The data were collected from Spatial Data Infrastructure (SDI) and Geographic Information System (GIS) experts. The findings underscored challenges related to human resources, SDI education/culture, long-term vision, lack of awareness of geoinformation (GI), sustainable funding, metadata availability, online geospatial services, and geospatial standards hindering NSDI development in Pakistan. However, certain factors exhibit favorable standings, such as the legal framework for NSDI establishment, web connectivity, geospatial software availability, the unavailability of core spatial datasets, and institutional leadership. Thus, to enhance the development of NSDI in Pakistan, recommendations include bolstering financial and human resources, improving online geospatial presence, and fostering a long-term vision for NSDI. Full article

This paper explores the application of The World Avatar (TWA) dynamic knowledge graph to connect isolated data and assess the impact of rising sea levels in Singapore. Current sea level rise vulnerability assessment tools are often regional, narrow in scope (e.g., economic or cultural aspects only), and are inadequate in representing complex non-geospatial data consistently. We apply TWA to conduct a multi-perspective impact assessment of sea level rise in Singapore, evaluating vulnerable buildings, road networks, land plots, cultural sites, and populations. We introduce OntoSeaLevel, an ontology to describe sea level rise scenarios, and its impact on broader elements defined in other ontologies such as buildings (OntoBuiltEnv ontology), road networks (OpenStreetMap ontology), and land plots (Ontoplot and Ontozoning ontology). We deploy computational agents to synthesise data from government, industry, and other publicly accessible sources, enriching buildings with metadata such as property usage, estimated construction cost, number of floors, and gross floor area. An agent is applied to identify and instantiate the impacted sites using OntoSeaLevel. These sites include vulnerable buildings, land plots, cultural sites, and populations at risk. We showcase these sea level rise vulnerable elements in a unified visualisation, demonstrating TWA’s potential as a planning tool against sea level rise through vulnerability assessment, resource allocation, and integrated spatial planning. Full article

The improvement of search engines for geospatial data on the World Wide Web has been a subject of research, particularly concerning the challenges in discovering and utilizing geospatial web services. Despite the establishment of standards by the Open Geospatial Consortium (OGC), the implementation of these services varies significantly among providers, leading to issues in dataset discoverability and usability. This paper presents a proof of concept for a search engine tailored to geospatial services in Switzerland. It addresses challenges such as scraping data from various OGC web service providers, enhancing metadata quality through Natural Language Processing, and optimizing search functionality and ranking methods. Semantic augmentation techniques are applied to enhance metadata completeness and quality, which are stored in a high-performance NoSQL database for efficient data retrieval. The results show improvements in dataset discoverability and search relevance, with NLP-extracted information contributing significantly to ranking accuracy. Overall, the GeoHarvester proof of concept demonstrates the feasibility of improving the discoverability and usability of geospatial web services through advanced search engine techniques. Full article

Geospatial data, especially remote sensing (RS) data, are of significant importance for public services and production activities. Expertise is critical in processing raw data, generating geospatial information, and acquiring domain knowledge and other remote sensing applications. However, existing geospatial service platforms are more oriented towards the professional users in the implementation process and final application. Building appropriate geographic applications for non-professionals remains a challenge. In this study, a geospatial data service architecture is designed that links desktop geographic information system (GIS) software and cloud-based platforms to construct an efficient user collaboration platform. Based on the scalability of the platform, four web apps with different themes are developed. Data in the fields of ecology, oceanography, and geology are uploaded to the platform by the users. In this pilot phase, the gap between non-specialized users and experts is successfully bridged, demonstrating the platform’s powerful interactivity and visualization. The paper finally evaluates the capability of building spatial data infrastructures (SDI) based on GeoNode and discusses the current limitations. The support for three-dimensional data, the improvement of metadata creation and management, and the fostering of an open geo-community are the next steps. Full article

In the digital transformation era, video media libraries’ untapped potential is immense, restricted primarily by their non-machine-readable nature and basic search functionalities limited to standard metadata. This study presents a novel multimodal methodology that utilizes advances in artificial intelligence, including neural networks, computer vision, and natural language processing, to extract and geocode geospatial references from videos. Leveraging the geospatial information from videos enables semantic searches, enhances search relevance, and allows for targeted advertising, particularly on mobile platforms. The methodology involves a comprehensive process, including data acquisition from ARD Mediathek, image and text analysis using advanced machine learning models, and audio and subtitle processing with state-of-the-art linguistic models. Despite challenges like model interpretability and the complexity of geospatial data extraction, this study’s findings indicate significant potential for advancing the precision of spatial data analysis within video content, promising to enrich media libraries with more navigable, contextually rich content. This advancement has implications for user engagement, targeted services, and broader urban planning and cultural heritage applications. Full article

This paper presents a holistic guiding methodology for the development of a geospatial tool to be used for the documentation, planning, smart management and dissemination of a country’s network of historic hiking trails. To deal with the challenges and to ensure the sustainability of a historic site, geospatial documentation merging authoritative and crowdsourced data and a WebGIS-based spatial analysis is necessary. Geospatial data collection should include professional field surveys, professional and crowdsourced photographic documentation and video recording of the existing historic walking/hiking trails. A geodatabase, structured using relational model technology, including vector spatial entities (feature classes), mosaics (raster) and tabulated data (geodatabase tables), should be developed on a commercial or open platform; in this case, the ArcGIS Pro is used. Entities with embedded descriptive information and metadata for the technical, legal, historical, and administrative context may then be created. An object-oriented data model is needed to connect spatial and descriptive information. Spatial and descriptive queries or correlations between attribute fields of spatial entities must be enabled for specialized information retrieval by either experts or users. Next, a web GIS application to present the developed geodatabase in a visually appealing and informative way is created. It should integrate 2D maps with built-in tools and should support advanced functionalities, such as: (i) pop-ups that display brief information and images about specific spots along the trails; (ii) dynamic visualization of the vertical profile of each trail; (iii) multimedia information about landmarks, natural features and scenic viewpoints. Finally, the tool includes a feedback service and continuous efficiency monitoring and assessment, and enables adjustments, if and where needed. The tool is tested and used for 10 historical walking/hiking trails of the archaeological and Holy Site of Meteora, Central Greece. This is a UNESCO World Heritage site. The network, with a total length of 35 km, leads to six monasteries, still active since the 12th century, passing by gigantic rocks and beautiful natural landscapes. The site is famous globally and the greater area is continuously overcrowded with visitors. The tool is anticipated to be used for the documentation and management of the whole walking/hiking historic trail network of Greece in the future. Full article

National spatial data infrastructure (NSDI) is an essential framework for managing and sharing geospatial data across different sectors and organizations. In Bangladesh, the development of NSDI is still in its early stages, and there are several challenges that need to be addressed to ensure its effective implementation. This paper provides a comprehensive assessment of the status of NSDI implementation in Bangladesh using Eelderink’s fourteen key variables. The paper examines the current state of NSDI implementation in Bangladesh, identifies strengths and weaknesses, and suggests recommendations for improvement. The findings suggest that while some progress has been made in establishing NSDI in Bangladesh, there are still significant challenges, such as limited funding; weak coordination among stakeholders; and a lack of skilled manpower, awareness, and capacity among users. To address these challenges, in this paper, we recommend several measures to improve the NSDI framework in Bangladesh. These include increasing funding support for NSDI development and maintenance, improving coordination among stakeholders through the establishment of a national coordinating body, enhancing awareness and capacity-building programs for NSDI users, and promoting the use of open data standards to improve data quality and interoperability. It is hoped that these recommendations will be taken into consideration by policymakers and other stakeholders to further enhance the development of NSDI in Bangladesh. Full article

Recent developments in Web Service and Semantic Web technologies have shown great promise for the automatic chaining of geographic information services (GIService), which can derive user-specific information and knowledge from large volumes of data in the distributed information infrastructure. In order for users to have an informed understanding of products generated automatically by distributed GIServices, provenance information must be provided to them. This paper describes a three-level conceptual view of provenance: the automatic capture of provenance in the semantic execution engine; the query and inference of provenance. The view adapts well to the three-phase procedure for automatic GIService composition and can increase understanding of the derivation history of geospatial data products. Provenance capture in the semantic execution engine fits well with the Semantic Web environment. Geospatial metadata is tracked during execution to augment provenance. A prototype system is implemented to illustrate the applicability of the approach. Full article

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset—the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85–89% to 90–94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF). Full article

Multiple scientific disciplines face a so-called crisis of reproducibility and replicability (R&R) in which the validity of methodologies is questioned due to an inability to confirm experimental results. Trust in information technology (IT)-intensive workflows within geographic information science (GIScience), remote sensing, and photogrammetry depends on solutions to R&R challenges affecting multiple computationally driven disciplines. To date, there have only been very limited efforts to overcome R&R-related issues in remote sensing workflows in general, let alone those tied to unmanned aircraft systems (UAS) as a disruptive technology. This review identifies key barriers to, and suggests best practices for, R&R in geospatial UAS workflows as well as broader remote sensing applications. We examine both the relevance of R&R as well as existing support for R&R in remote sensing and photogrammetry assisted UAS workflows. Key barriers include: (1) awareness of time and resource requirements, (2) accessibility of provenance, metadata, and version control, (3) conceptualization of geographic problems, and (4) geographic variability between study areas. R&R in geospatial UAS applications can be facilitated through augmented access to provenance information for authorized stakeholders, and the establishment of R&R as an important aspect of UAS and related research design. Where ethically possible, future work should exemplify best practices for R&R research by publishing access to open data sets and workflows. Future work should also explore new avenues for access to source data, metadata, provenance, and methods to adapt principles of R&R according to geographic variability and stakeholder requirements. Full article

This paper presents an approach to GWS (GeospatialWeb Service) discovery through the semantic annotation of WPS (Web Processing Service) service descriptions. The rationale behind this work is that search engines that use appropriate semantic-based similarity measures in the matching process are more accurate in terms of precision and recall than those based on syntactic matching alone. The lack of semantics in the description of services using a standard such as WPS prevents the use of such a matching process and is considered a limitation of GWS discovery. The GWS discovery approach presented is based on the consideration of semantics in the service description method and in the matching process. The description of services is based on a semantic lightweight meta-model instantiated in the WPS 2.0 standard, extending the description of the service through metadata tags. The matching process is performed in three steps (functionality matching step, I/O (Input/Output) matching step and non-functional matching step). Its core is a semantic similarity measure that combines logical and non-logical matching methods. Finally, the paper presents the results of an experiment applying the proposed discovery approach on a GWS corpus, showing promising results and the added value of the three-step matching process. Full article

The discrete representation of resources in geospatial catalogues affects their information retrieval performance. The performance could be improved by using automatically generated clusters of related resources, which we name quasi-spatial dataset series. This work evaluates whether a clustering process can create quasi-spatial dataset series using only textual information from metadata elements. We assess the combination of different kinds of text cleaning approaches, word and sentence-embeddings representations (Word2Vec, GloVe, FastText, ELMo, Sentence BERT, and Universal Sentence Encoder), and clustering techniques (K-Means, DBSCAN, OPTICS, and agglomerative clustering) for the task. The results demonstrate that combining word-embeddings representations with an agglomerative-based clustering creates better quasi-spatial dataset series than the other approaches. In addition, we have found that the ELMo representation with agglomerative clustering produces good results without any preprocessing step for text cleaning. Full article

With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it’s still a big challenge to manage and process such huge amounts of remote sensing data with complex and diverse structures. This paper designs and realizes a distributed storage system for large-scale remote sensing data storage, access, and retrieval, called RSIMS (remote sensing images management system), which is composed of three sub-modules: RSIAPI, RSIMeta, RSIData. Structured text metadata of different remote sensing images are all stored in RSIMeta based on a set of uniform models, and then indexed by the distributed multi-level Hilbert grids for high spatiotemporal retrieval performance. Unstructured binary image files are stored in RSIData, which provides large scalable storage capacity and efficient GDAL (Geospatial Data Abstraction Library) compatible I/O interfaces. Popular GIS software and tools (e.g., QGIS, ArcGIS, rasterio) can access data stored in RSIData directly. RSIAPI provides users a set of uniform interfaces for data access and retrieval, hiding the complex inner structures of RSIMS. The test results show that RSIMS can store and manage large amounts of remote sensing images from various sources with high and stable performance, and is easy to deploy and use. Full article

Geospatial data is used not only to contemplate reality but also, in combination with analytical tools, to generate new information that requires interpretation. In this process data users gain knowledge about the data and its limitations (the user side of data quality) as well as knowledge on the status and evolutions of the studied phenomena. Knowledge can be annotations on top of the data, responses to questions, a careful description of the processes applied, a piece of software code or scripts applied to the data, usage reports or a complete scientific paper. This paper proposes an extension of the current Open Geospatial Consortium standard for Geospatial User Feedback to include the required knowledge elements, and a practical implementation. The system can incrementally collect, store, and communicate knowledge elements created by users of the data and keep them linked to the original data by means of permanent data identifiers. The system implements a Web API to manage feedback items as a frontend to a database. The paper demonstrates how a JavaScript widget accessing this API as a client can be easily integrated into existing data catalogues, such as the ECOPotential web service or the GEOEssential data catalogue, to collectively collect and share knowledge. Full article