Computational tools for urban analysis
A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python
Land surface classification using remote sensing data with unsupervised machine learning (k-means).
Accessibility Toolbox for R and ArcGIS
Calculate accessibility from OD matrix on Python
OpenStreetMap: Find residential areas with too few buildings in them
Developing a modelling system to quantify features of land use in urban environments, UK based
We provide a pixel level training dataset for landuse classification (four categories - Green, Water, Barren land and Built up Areas) using google earth engine for India. All associated scripts are also provided.
An interface for managing SWATPlus input and output files to aid in implementing, and visualizing the impact of land use changes on catchment hydrology in the SWATPlus model
A post-processing, interactive visualization, and analysis tool to synthesize multi-scenario, multi-watershed outputs from process-based geospatial models WEPP and SWAT
TMG's Integrated Land Use, Transportation, Environment
Ressources pour l'exploitation de l'occupation du sol à 2 dimensions des Hauts-de-France
Tools for extracting and preparing Digital Earth Australia Satellite Multi-Spectral Images for use in Deep Learning Machine models.
Synthesize multi-scenario, multi-watershed outputs from process-based geospatial model WEPP (WEPPcloud) using this post-processing, interactive visualization, and analysis tool. A Shiny Web app implementation to assist in targeted management using WEPPcloud simulated outputs.
Spatial analysis of agricultural land use trends in Illinois with a focus on the Chicagoland area and collar counties in northeastern Illinois.
Google Earth Engine Application in the field of Climate change and Earth system monitoring by analysing climatic, physical and biophysical data.
Code and data used during the hackathon Code4Green by the team team_cli07_landscapeoptimizer
Environmental data visualisation and exploration
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