teamlucc
by azvoleff
Analysis of land use and cover change using satellite imagery
AI summary
Land use analyzer
Analyzes land use and cover change from satellite imagery
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Similar projects
Found by comparing what the projects do, not just their names.
Land use analyzer
An R package for analyzing land use and cover change, providing tools for visualization and intensity analysis of time series data.
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Land use analysis toolkit
A collection of data and code for analyzing national zoning and land use patterns
Change detection algorithm
An algorithm for detecting and classifying changes in land cover using Landsat data, requiring processing of large datasets and generating output files.
Land cover change detector
This MATLAB implementation detects changes in various types of land cover in satellite images using PCA and k-Means clustering
Land analysis toolkit
Develops algorithms and integrates satellite data from Google Earth Engine to analyze land surface changes and track environmental patterns.
Satellite viewer
An application that allows users to view and analyze Landsat 8 satellite imagery in the browser using WebGL.
Land use mapper
An R package providing a method to analyze satellite image time series by leveraging Time-Weighted Dynamic Time Warping and a 1 Nearest-Neighbor classifier for land use mapping.
Cover analyzer
A remote sensing algorithm to separate and quantify land cover components from satellite images.
Landscape analyzer
A Python library for analyzing landscape metrics and evolution
forest analysis tool
Provides a set of functions to estimate forest structural attributes from airborne LiDAR point clouds
Land analysis tool
An online tool to analyze land change and track progress towards sustainable development goals
Satellite analysis
Analyze satellite imagery to investigate environmental and humanitarian challenges
Landscape analyzer
A package for calculating landscape metrics in R, providing a reproducible workflow for analyzing categorical landscape patterns.
Change Detection
Automatically inferring 2D and 3D change detection maps from bitemporal optical images without relying on DSMs.
Featured in 2 awesome lists
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