Stars
Techniques for deep learning with satellite & aerial imagery
Python package for earth-observing satellite data processing
DEPRECATED: Please use xDEM : https://github.com/Glaciohack/xdem
Comprehensive language-agnostic guidelines on variables naming. Home of the A/HC/LC pattern.
Earth observation processing framework for machine learning in Python
Sentinel Hub Cloud Detector for Sentinel-2 images in Python
The SOS-ABS code (Successive Orders of Scattering method coupled with the gaseous ABSorptions) is a 1D plan parallel radiative transfer code simulating the polarized radiance of the {Earth surface …
The Active Learning for Cloud Detection (ALCD) software enables to generate reference cloud masks which may be used to validate operational cloud masks, such as those generated by MAJA. The referen…
Ocean Successive Orders with Atmosphere - Advanced (OSOAA) : Accurate computation of the radiative transfer into the Ocean-Atmosphere system including polarisation, coupling terms, and the agitated…
Building Analysis Ready Data of Sentinel-1 time series
CARS is a dedicated and open source 3D tool to produce Digital Surface Models from satellite imaging by photogrammetry.
A stereo matching framework that will help you design your stereo matching pipeline with state of the art performances.
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.
curated list of awesome tools, tutorials and APIs for Copernicus Sentinel satellite data
WASP (Weighted Average Synthesis Processor) is a C++/Python software to produce monthly cloud free images from Sentinel-2 Level 2A data obtained with MAJA software. WASP has been designed to minimi…
Sirius - Fast and simple to plug-in C++ resampling library that is taking advantage of the Fourier Transform
Github mirror of https://gitlab.orfeo-toolbox.org/orfeotoolbox/otb