Deliverables
GEM portal will make all available public products and documents publicly available to a user community wider than the one already involved in the projectSINERGISE will setup GEM project page and later manage it throughout the project
New data gateways will be designeddeveloped to enable ingestion of Very HighResolution data by Sentinel Hub Draft list of sources to be supported PlanetScope SPOT Pleiades and WorldView
TUM will contribute their existent data loaders interfaces and wrapper functions helping SINERGISE to accelerate the development of ML gateways towards external ML frameworksThe exact list of ML frameworks will be fixed after the data modelling requirements will be finalised Current list of potential ML frameworks to be integrated consists ofTENSORFLOW Googles TensorFlow is the most widely used framework for machine learning based on Theano see below KERAS is an open source neural network library written in Python It is designed to enable fast experimentation with deep neural networks it focuses on being userfriendly modular and extensible APACHE MXNet is a modern opensource machine learning framework used to train and deploy deep neural networksPyTorch primarily developed by Facebooks AI Research lab FAIR is free and opensource software ermarked by two highlevel features Tensor computing and Deep neural networks
Cloud masking algorithms will be included into EOLEARN to enable pseudoprobability cloud masking of Sentinel dataFollowing it Sentinel Hubs preprocessing chains will be updated to integrate new pseudoprobability Cloud masking and make it readily available for complete Sentinel2 archive
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Publications
Author(s): M. Schneider, A. Broszeit, M. Korner
Published in: 2021
Publisher: TUM
Author(s): S. Albani, O. Barrilero, M. Lazzarini, A. Luna, P. Saameño
Published in: 2021
Publisher: satcen
Author(s): S. Albani, O. Barrilero, M. Lazzarini, A. Luna, P. Saameño
Published in: 2021
Publisher: satcen