Massively-parallel break detection for satellite data
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Massively-parallel break detection for satellite data. / von Mehren, Malte; Gieseke, Fabian; Verbesselt, Jan; Rosca, Sabina; Horion, Stéphanie; Zeileis, Achim.
SSDBM '18 Proceedings of the 30th International Conference on Scientific and Statistical Database Management . Vol. Part F137913 Association for Computing Machinery, 2018. 5.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Massively-parallel break detection for satellite data
AU - von Mehren, Malte
AU - Gieseke, Fabian
AU - Verbesselt, Jan
AU - Rosca, Sabina
AU - Horion, Stéphanie
AU - Zeileis, Achim
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85051254091&partnerID=8YFLogxK
U2 - 10.1145/3221269.3223032
DO - 10.1145/3221269.3223032
M3 - Article in proceedings
VL - Part F137913
BT - SSDBM '18 Proceedings of the 30th International Conference on Scientific and Statistical Database Management
PB - Association for Computing Machinery
T2 - 30th International Conference on Scientific and Statistical Database Management, SSDBM 2018
Y2 - 9 July 2018 through 11 July 2018
ER -
ID: 203675655