There are 2 repositories under lulc topic.
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.
This repository will guide you how to use deep learning algorithms for land use land cover classification using satellite dataset!
Repository for Amazon biome classification codes.
Repositório usado durante o treinamento do mapbiomas-chile
This python module extracts land use land cover (LULC) type using Copernicus or MODIS LULC products.
Bull-ProFlora is a job message queue system designed at CNCFlora/JBRJ (Rio de Janeiro, Brazil) to streamline species extinction risk assessments of Brazilian flora. Its architecture is tailored to handle large-scale data processing efficiently, ensuring seamless integration with the ProFlora system.
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
Analytics based on Dynamic World LULC derived from Sentinel - 2 images
Experimentation of LULC classification using DL techniques
Official code of the paper "Self-Supervised Learning on Small In-Domain Datasets Can Overcome Supervised Learning in Remote Sensing."
https://erisonbarros.github.io/BR104_OCUP_FAIXA_DOMINIO/
This repository is intended to provide land use/land cover creators with a set of tools to facilitate their tasks.