A ready-to-use curated list of Spectral Indices for Remote Sensing applications.

Global shoreline mapping tool from satellite imagery

The Clay Foundation Model - An open source AI model and interface for Earth

Satellite image time series in R

Open-source Jupyter Notebooks and Python tools for geospatial analysis with Digital Earth Australia, Open Data Cube, and Xarray

Sentinel Hub Cloud Detector for Sentinel-2 images in Python

🌱 Deep Learning for Instance Segmentation of Agricultural Fields - Master thesis

Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and spectral indices in a sensor-agnostic way.

A collection of all earth related space Images in one script to set as your Desktop background.

Expandable Datasets for Earth Observation

The official repository for the EuroCrops dataset.

PyTorch implementation of U-TAE and PaPs for satellite image time series panoptic segmentation.

On-Demand Earth System Data Cubes (ESDCs) in Python

This repository contains the code used in the paper: A high-resolution canopy height model of the Earth. Here, we developed a model to estimate canopy top height anywhere on Earth. The model estimates canopy top height for every Sentinel-2 image pixel and was trained using sparse GEDI LIDAR data as a reference.

DSen2-CR: A network for removing clouds from Sentinel-2 images. This repo contains the model code, written in Python/Keras, as well as links to pre-trained checkpoints and the SEN12MS-CR dataset.

OmniCloudMask is a Python library for fast, accurate cloud and cloud shadow segmentation in satellite imagery.

a deep model that segments water on multispectral images

The STARFM fusion model for Python

Level-2A processor used for atmospheric correction and cloud-detection. The active repository is the one below, this one is kept to leave access to the older issues.

A deep learning model for surface water mapping based on satellite optical image.