Application of deep learning for earth observation.

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

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 contains the official implementation of the paper "LandSegmenter: Towards a Flexible Foundation Model for Land Use and Land Cover Mapping".

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 project uses a U-Net CNN to classify land use for the entire City ot Toronto at high-resolution in an automated pipeline.

Moirai - Land Data System

Repository for Amazon biome classification codes.

LULC samples for the Restore+ project dataset.

Repositório usado durante o treinamento do mapbiomas-chile

This repository provides the data processing pipelines and Python scripts required to reproduce the quantitative modeling of capitalist land enclosure in PIK2, Indonesia, by applying information geometry, Markov chains, and percolation theory to Sentinel-2 land use data.

A Google Earth Engine Land use (crops) classification workflow using Random Forest, one year of ground data, Sentinel-2, and Landsats; to produce multiyear annual 30-m crop maps

Tool to enrich land-use/land-cover data with historical data, OpenStreetMap and protected areas

Deep learning pipeline (UNet) for built-up area extraction from Sentinel-2 imagery — applied to Chennai for urban heat island and climate risk analysis

Methodology description of the Mapbiomas' industrial and artisanal mining detection target

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

This repository contains an academic field-based GIS project on Dulahajara Mouza, including land use data collection, digitization in ArcGIS, and a socio-economic survey to understand how land use patterns relate to local livelihoods and development.

Trained EfficientNet models, achieving up to 98% validation accuracy in land cover classification.

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

LULC Classification and Flood Vulnerability Analysis of Malappuram District (2018–2024) using Sentinel-2 and CART Machine Learning

Methodology description of the Mapbiomas' aquaculture detection target

Record of own applications

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.

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

Spatio-temporal analysis of land use and land cover changes using GIS and remote sensing techniques.

A shiny application to explore Land Use and Land Cover data

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

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