Download and process satellite imagery in JavaScript or TypeScript using Sentinel Hub services.
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Updated
May 23, 2024 - TypeScript
Download and process satellite imagery in JavaScript or TypeScript using Sentinel Hub services.
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 pure Julia package for querying and downloading Sentinel data.
A Python API for data discovery on Copernicus Data Space Ecosystem
Automatization of ESA Sentinel-3 images query and download
Automatization of the SEN-et SNAP plugin for multi-temporal analysis
Repository of WEkEO Jupyter Notebooks for learning about the Sentinel-3 OLCI sensor for marine applications
Quick data visualisation of thermal data for airborne campaign planning
Repository of WEkEO Jupyter Notebooks for machine learning applications with EUMETSAT Copernicus marine data, in support of the 2020 Liege Colloquium.
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
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
Repository of WEkEO Jupyter Notebooks for learning about the Sentinel-3 SLSTR sensor for marine applications
The STARFM fusion model for Python
Tool to download Sentinel images from PEPS sentinel mirror site : https://peps.cnes.fr
Chl-tsm with C2RCC processor (OLCI-Sentinel 3). SNAP Guide
Unsupervised and supervised learning for satellite image classification
Mostly used optical indices with satellite images for studying earth surface and different features and their changes in spatio-temporal scale.
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
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