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Massively-parallel break detection for satellite data

Published: 09 July 2018 Publication History

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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Cited By

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  • (2023)Continuous burned area monitoring using bi-temporal spectral index time series analysisInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2023.103547125(103547)Online publication date: Dec-2023
  • (2022)A Downsampling Method Addressing the Modifiable Areal Unit Problem in Remote SensingRemote Sensing10.3390/rs1421553814:21(5538)Online publication date: 3-Nov-2022
  • (2020)Massively-Parallel Change Detection for Satellite Time Series Data with Missing Values2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00040(385-396)Online publication date: Apr-2020

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cover image ACM Other conferences
SSDBM '18: Proceedings of the 30th International Conference on Scientific and Statistical Database Management
July 2018
314 pages
ISBN:9781450365055
DOI:10.1145/3221269
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 09 July 2018

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SSDBM '18 Paper Acceptance Rate 30 of 75 submissions, 40%;
Overall Acceptance Rate 56 of 146 submissions, 38%

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Cited By

View all
  • (2023)Continuous burned area monitoring using bi-temporal spectral index time series analysisInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2023.103547125(103547)Online publication date: Dec-2023
  • (2022)A Downsampling Method Addressing the Modifiable Areal Unit Problem in Remote SensingRemote Sensing10.3390/rs1421553814:21(5538)Online publication date: 3-Nov-2022
  • (2020)Massively-Parallel Change Detection for Satellite Time Series Data with Missing Values2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00040(385-396)Online publication date: Apr-2020

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