Bull-ProFlora is a job 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 (https://proflora.jbrj.gov.br/).

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

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

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."

Tool for Quantitative Analysis and Visualization of Land Use and Land Cover Change.

Application of deep learning for earth observation.

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

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!

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.

Moirai - Land Data System

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

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

🌱 Using remote sensing data for catching the dynamics of vegetation restoration on the example of degraded boreal landscapes

Repositório usado durante o treinamento do mapbiomas-chile

This python module extracts land use land cover (LULC) type using Copernicus or MODIS LULC products.

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

Repository for Amazon biome classification codes.