This repository is intended to provide a set of tools to facilitate land use/land cover construction.
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Updated
Sep 27, 2024
This repository is intended to provide a set of tools to facilitate land use/land cover construction.
A collection of Imagery Explorer web applications developed by Esri's ArcGIS Living Atlas team
A high-level Python framework to evaluate the skill of geospatial datasets by comparing candidates to benchmark maps producing agreement maps and metrics.
Develope a CNN-GRU model to Predict Land cover
GRASS GIS addon for Incora landcover classification. See also https://github.com/mundialis/incora
Ressources pour l'exploitation de l'occupation du sol à 2 dimensions des Hauts-de-France
Ressources pour l'exploitation de l'occupation du sol à 2 dimensions des Hauts-de-France
GeoGraph provides a tool for analysing habitat fragmentation and related problems in landscape ecology. GeoGraph builds a geospatially referenced graph from land cover or field survey data and enables graph-based landscape ecology analysis as well as interactive visualizations.
Googgle Earth Engine App: 3D Wetlands App
🛣 Building an end-to-end Promptable Semantic Segmentation (Computer Vision) project from training to inferencing a model on LandCover.ai data (Satellite Imagery).
Landcover classification models validator using the SIGPAC data
🛣 Building an end-to-end Promptable Semantic Segmentation (Computer Vision) project from training to inferencing a model on LandCover.ai data (Satellite Imagery).
This code allows to compute 10+ indexes used in biodiversity metrics
Simple layers for species distribution modeling and bioclimatic data
Analysis for InsectMobile diversity and biomass across Denmark and Germany in the summer of 2018 and 2019
Study that examines the landscape-level effects of land cover and land use on flying insect biomass
The Short-term Forest Change Tool (STFC) is a Google Earth Engine script created by the Spring 2020 Costa Rica and Panama Ecological Forecasting team. The main scope of the software is to display changes in vegetation of forested areas and identify regions of possible deforestation.
The aim of this project was to create a land cover classification of the area near Surat in India for 3 timesteps (2015, 2018, 2022) using a Random Forest classifier to access the process of urbanization
Land classification using satellite imageries in QGIS
Repository for the analysis of the MS: "Detecting flying insects using car nets and DNA metabarcoding"
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