Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 540567
Title Massively-parallel break detection for satellite data
Author(s) Mehren, Malte von; Gieseke, Fabian; Verbesselt, Jan; Rosca, Sabina; Horion, Stéphanie; Zeileis, Achim
Source In: Proceedings of the 30th International Conference on Scientific and Statistical Database Management. - New York : Association for Computing Machinery - ISBN 9781450365055
Event New York : Association for Computing Machinery - ISBN 9781450365055 30th International Conference on Scientific and Statistical Database Management, SSDBM 2018, Bolzano-Bozen, 2018-07-09/2018-07-11
DOI https://doi.org/10.1145/3221269.3223032
Department(s) Laboratory of Geo-information Science and Remote Sensing
PE&RC
Publication type Contribution in proceedings
Publication year 2018
Abstract

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.

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