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

The STARFM fusion model for Python

Tool to download Sentinel images from PEPS sentinel mirror site : https://peps.cnes.fr

Download and process satellite imagery in JavaScript or TypeScript using Sentinel Hub services.

A Python API for data discovery on Copernicus Data Space Ecosystem

Repository of WEkEO Jupyter Notebooks for learning about EUMETSAT Copernicus satellite data for marine applications. This code is a partial mirror of the repositories on https://gitlab.eumetsat.int/eumetlab/oceans/ocean-training

Compare Spectrograms of Hyperspectral and Multispectral Satellite Missions

Repository of WEkEO Jupyter Notebooks for learning about the Sentinel-3 OLCI sensor for marine applications

Automatization of the SEN-et SNAP plugin for multi-temporal analysis

Deep learning for Synthetic Aperture Radar(SAR) and Radiometry data. An Ensemble Convolutional Neural Network workflow is implemented with data acquisition, processing, labelling, creating model, training model and launching a model

Unsupervised and supervised learning for satellite image classification

Repository of WEkEO Jupyter Notebooks for machine learning applications with EUMETSAT Copernicus marine data, in support of the 2020 Liege Colloquium.

Automatization of ESA Sentinel-3 images query and download

Repository of WEkEO Jupyter Notebooks for examples of marine case studies featuring EUMETSAT and/or CMEMS data.

Repository of WEkEO Jupyter Notebooks for learning about the Sentinel-3 SRAL sensor for marine applications

Mostly used optical indices with satellite images for studying earth surface and different features and their changes in spatio-temporal scale.

Repository of WEkEO Jupyter Notebooks for learning about the Sentinel-3 SLSTR sensor for marine applications

A pure Julia package for querying and downloading Sentinel data.

codes for JSTARS paper: Sentinel-3 Super-Resolution based on Dense Multi-Receptive Channel Attention

codes for JSTARS paper: Multitemporal Mosaicing for Sentinel-3/FLEX Derived Level-2 Product Composites