FireRS SAOP data files
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Updated
Jun 24, 2019
FireRS SAOP data files
Get started with landcover classification using machine learning and satellite or aerial imagery.
Harmonize classification raster files using Latent Dirichlet Allocation
Repository for the analysis of the MS: "Detecting flying insects using car nets and DNA metabarcoding"
Land classification using satellite imageries in QGIS
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
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.
Study that examines the landscape-level effects of land cover and land use on flying insect biomass
Analysis for InsectMobile diversity and biomass across Denmark and Germany in the summer of 2018 and 2019
Simple layers for species distribution modeling and bioclimatic data
This code allows to compute 10+ indexes used in biodiversity metrics
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).
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.
Develope a CNN-GRU model to Predict Land cover
This repository is intended to provide a set of QGIS tools to facilitate land use/land cover construction.
Cluster landcover change trajectories
a Webmap to present annual land cover dataset for the Baltic Sea Region with crop types and peat bogs at 30 m from 2000 to 2022
🛣 Building an end-to-end Promptable Semantic Segmentation (Computer Vision) project from training to inferencing a model on LandCover.ai data (Satellite Imagery).
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