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ExtremeEarth Meets Satellite Data From Space
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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One of these challenges is the integration of European Space Agency (ESA)'s Thematic Exploitation Platforms (TEPs) and data information access service platforms with a data platform, namely Hopsworks, ...
Bringing together a number of cutting-edge technologies that range from storing extremely large volumes of data all the way to developing scalable machine learning and deep learning algorithms in a distributed ...
He holds a Ph.D. in Computer Science from Hanover University and a Diploma in Computer Engineering from NTUA. His research focuses on data integration and web data mining. ...
doi:10.1109/jstars.2021.3107982
fatcat:fxmpayska5bvlj7ibw3peqhuzu
MACHINE LEARNING FOR SEA ICE MONITORING FROM SATELLITES
2019
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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</strong> Today, radar imaging from space allows continuous and wide-area sea ice monitoring under nearly all weather conditions. ...
ACKNOWLEDGEMENTS We will like to thank Nick Hughes from the Norwegian Meteorological Institute in Oslo, Norway (a partner in the H2020 ExtremeEarth project), for helpful discussions about the investigated ...
For each satellite image, this information is extracted from image patches (e.g., 128×128 pixels, 64×64 pixels) and ingested into a data base. ...
doi:10.5194/isprs-archives-xlii-2-w16-83-2019
fatcat:yaouvxdzsffazhnvnbuk6b5pcu
Knowledge Extracted from Copernicus Satellite Data
2019
Zenodo
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ExtremeEarth is a European H2020 project; it aims at developing analytics techniques and technologies that combine Copernicus satellite data with information and knowledge extraction, and exploiting them ...
During the development of deep learning algorithms, a key activity is to establish a large amount of referenced Earth Observation data. ...
One of the most difficult task is to distinguish the glaciers, glaciers lakes and dry from dry land. ...
doi:10.5281/zenodo.3941573
fatcat:zzifwgljifck5bpjnboetsftfu
Feature-free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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In the following, we consider the problem of unsupervised feature-free satellite image classification with already known classes as an explainable data mining problem for regions with no prior information ...
There is an increasing demand toward explainable machine learning models as they strive to meet the "right to explanation." ...
He is involved in big data from Space European, ESA, NASA, and national research programs and projects. Dr. ...
doi:10.1109/jstars.2020.3039012
fatcat:eynukzau7jgebnxxquh35c4axa
Accelerate Model Parallel Training by Using Efficient Graph Traversal Order in Device Placement
[article]
2022
arXiv
pre-print
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One has to transform graph data from the multi-dimensional space into a sequence of nodes before the majority of the DL methods can consume the graph data. ...
EO satellites developed over the years have provided an unprecedented amount of data that need to be processed [11] , [12] . ...
arXiv:2201.09676v1
fatcat:d7l6yg4qjfa5philr5k5hb3c34
ETP4HPC's Strategic Research Agenda for High-Performance Computing in Europe 4
[article]
2020
Zenodo
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The main objective of this SRA is to identify the European technology research priorities in the area of HPC and High-Performance Data Analytics (HPDA), which should be used by EuroHPC to build its 2021 ...
competitive European HPC systems but also about making our HPC solutions work together with other related technologies - the material included in this SRA is also a result of our interactions with Big Data ...
spaces. ...
doi:10.5281/zenodo.4605343
fatcat:lcsgbea5dzgdfmj5dkw6pr7vni