Nothing Special   »   [go: up one dir, main page]

Flynn et al., 2020 - Google Patents

Input map and feature selection for soil legacy data

Flynn et al., 2020

Document ID
14385944270954978177
Author
Flynn T
Rozanov A
Clarke C
Publication year
Publication venue
Geoderma

External Links

Snippet

Techniques that disaggregate complex soil-terrain polygons from legacy maps are becoming more relevant, as cost effective highly detailed soil information is required to advise agriculture, hydrology, ecology, engineering, and a variety of other disciplines …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30241—Information retrieval; Database structures therefor; File system structures therefor in geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/05—Geographic models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
    • G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation

Similar Documents

Publication Publication Date Title
Cao et al. Susceptibility assessment of landslides triggered by earthquakes in the Western Sichuan Plateau
Jochem et al. Identifying residential neighbourhood types from settlement points in a machine learning approach
Marjanović et al. Landslide susceptibility assessment using SVM machine learning algorithm
Jafari et al. Selection of a taxonomic level for soil mapping using diversity and map purity indices: a case study from an Iranian arid region
Maxwell et al. Predicting palustrine wetland probability using random forest machine learning and digital elevation data-derived terrain variables
Azar et al. Spatial refinement of census population distribution using remotely sensed estimates of impervious surfaces in Haiti
Lu et al. An integrated method of selecting environmental covariates for predictive soil depth mapping
Flynn et al. Semi-automatic disaggregation of a national resource inventory into a farm-scale soil depth class map
Liang et al. A case-based method of selecting covariates for digital soil mapping
Munyati Optimising multiresolution segmentation: delineating savannah vegetation boundaries in the Kruger National Park, South Africa, using Sentinel 2 MSI imagery
Flynn et al. Input map and feature selection for soil legacy data
Goldman et al. Digital soil mapping in a low-relief landscape to support wetland restoration decisions
Aahlaad et al. An object-based image analysis of worldview-3 image for urban flood vulnerability assessment and dissemination through ESRI story maps
Bernard et al. Estimation of missing building height in OpenStreetMap data: a French case study using GeoClimate 0.0. 1
Cremon et al. Geological and terrain attributes for predicting soil classes using pixel-and geographic object-based image analysis in the Brazilian Cerrado
Jamshidi et al. Disaggregation of conventional soil map by generating multi realizations of soil class distribution (case study: Saadat Shahr plain, Iran)
Yost Probabilistic modeling and mapping of plant indicator species in a Northeast Oregon industrial forest, USA
Beekhuizen et al. Toward accountable land use mapping: Using geocomputation to improve classification accuracy and reveal uncertainty
Crols et al. Downdating high-resolution population density maps using sealed surface cover time series
Zhou et al. Automated soil resources mapping based on decision tree and Bayesian predictive modeling
Odeh et al. GIS-based analytical analysis for selecting potential runoff harvesting sites: the case study of Amman-Zarqa Basin
BARRINGER et al. National mapping of landform elements in support of S-Map, a New Zealand soils database
Shanmuganathan A hybrid artificial neural network (ANN) approach to spatial and non-spatial attribute data mining: A case study experience
Li et al. Large-scale extraction of check dams and silted fields on the Chinese loess plateau using ensemble learning models
CN113537793A (en) Method for ecological hydrological zoning of drainage basin