Visualize classified time series data with interactive Sankey plots in Google Earth Engine

Application of deep learning for earth observation.

A Colab notebook for land cover mapping and monitoring using Earth Engine

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)"

This repository will guide you how to use deep learning algorithms for land use land cover classification using satellite dataset!

Moirai - Land Data System

Repository for Amazon biome classification codes.

Repositório usado durante o treinamento do mapbiomas-chile

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.

Official code of the paper "Self-Supervised Learning on Small In-Domain Datasets Can Overcome Supervised Learning in Remote Sensing."

🌱 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.

This repository is intended to provide a set of QGIS tools to facilitate land use/land cover construction.

Experimentation of LULC classification using DL techniques

Land Use and Land Cover (LULC) classification using a shallow CNN, and incorporation of domain knowledge of Remote Sensing

https://erisonbarros.github.io/BR104_OCUP_FAIXA_DOMINIO/

Analytics based on Dynamic World LULC derived from Sentinel - 2 images