Massively-parallel break detection for satellite data

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

The field of remote sensing is nowadays faced with huge amounts of data. While this offers a variety of exciting research opportunities, it also yields significant challenges regarding both computation time and space requirements. In practice, the sheer data volumes render existing approaches too slow for processing and analyzing all the available data. This work aims at accelerating BFAST, one of the state-of-the-art methods for break detection given satellite image time series. In particular, we propose a massively-parallel implementation for BFAST that can effectively make use of modern parallel compute devices such as GPUs. Our experimental evaluation shows that the proposed GPU implementation is up to four orders of magnitude faster than the existing publicly available implementation and up to ten times faster than a corresponding multi-threaded CPU execution. The dramatic decrease in running time renders the analysis of significantly larger datasets possible in seconds or minutes instead of hours or days. We demonstrate the practical benefits of our implementations given both artificial and real datasets.

TitelSSDBM '18 Proceedings of the 30th International Conference on Scientific and Statistical Database Management
Antal sider10
Vol/bindPart F137913
ForlagAssociation for Computing Machinery
ISBN (Elektronisk)9781450365055
StatusUdgivet - 2018
Begivenhed30th International Conference on Scientific and Statistical Database Management, SSDBM 2018 - Bolzano-Bozen, Italien
Varighed: 9 jul. 201811 jul. 2018


Konference30th International Conference on Scientific and Statistical Database Management, SSDBM 2018
SponsorAlpin, EOS Solutions, Systems, Wurth Phoenix

ID: 203675655