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Global Earth Monitor


GEM portal

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

Integration of VHR sources

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

DEMO dataset

Project Demo regions 10 and 1 MIO km2 areas will be selected based on analysis of available data A set of sites will be selected to allow benchmarking the methods and algorithms with regard to the diversity of LC context the various landscape patterns ecoregionalisation patterns actual satellite observation conditions atmospheric perturbations and cloud coverage availability of insitu observation and verification capabilities of consortium The challenge is to allow identifying the most appropriate regions which would enable scalingup to the global scale For obvious reason it is also very important to select sites where historical insitu and satellites time series are already available or at least will be ready with the star of the project The answers on who how and when we will prepare certain datasets are the most important ingredients of the documentFor the selected DEMO areas all of the dataset will be collected or collection services established

EO-LEARN Gateways

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

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


"Eurocrops: a pan-European Dataset for time series crop type classification"", Proceedings of BiDS 2021"

Author(s): M. Schneider, A. Broszeit, M. Korner
Published in: 2021
Publisher: TUM

Integration of EO and ancillary datafor a climate security scenario: the Sahel case study

Author(s): S. Albani, Luna, M. Lazzarini, N. Baselovic, O. Barrilero, P. Saameno, M. Madrid, A. Patrono
Published in: 2023
Publisher: IEEE

"Exploring the climate-security nexus with spaceborne data"", Proceedings of BiDS 2021."

Author(s): S. Albani, O. Barrilero, M. Lazzarini, A. Luna, P. Saameño
Published in: 2021
Publisher: satcen

"A Digital Twin Earth For Security: From data to information"", Proceedings of BiDS 2021"

Author(s): S. Albani, O. Barrilero, M. Lazzarini, A. Luna, P. Saameño
Published in: 2021
Publisher: satcen

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