Visualize classified time series data with interactive Sankey plots in Google Earth Engine
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Updated
Mar 13, 2024 - Python
Visualize classified time series data with interactive Sankey plots in Google Earth Engine
Application of deep learning for earth observation.
Tool for Quantitative Analysis and Visualization of Land Use and Land Cover Change.
A repository containing data for the paper" Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020)"
Repository for Amazon biome classification codes.
This repository will guide you how to use deep learning algorithms for land use land cover classification using satellite dataset!
This is a Google Earth Engine (GEE) code written in JavaScript. The code primarily focuses on processing Landsat satellite imagery for the year 1990, including cloud masking, calculating vegetation indices (NDVI and NDBI), and implementing a Random Forest classifier for land cover classification.
🌱 Using remote sensing data for catching the dynamics of vegetation restoration on the example of degraded boreal landscapes
This python module extracts land use land cover (LULC) type using Copernicus or MODIS LULC products.
Analytics based on Dynamic World LULC derived from Sentinel - 2 images
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