TY - GEN

T1 - Massively-parallel change detection for satellite time series data with missing values

AU - Gieseke, Fabian

AU - Rosca, Sabina

AU - Henriksen, Troels

AU - Verbesselt, Jan

AU - Oancea, Cosmin E.

PY - 2020

Y1 - 2020

N2 - Large amounts of satellite data are now becoming available, which, in combination with appropriate change detection methods, offer the opportunity to derive accurate information on timing and location of disturbances such as deforestation events across the earth surface. Typical scenarios require the analysis of billions of image patches/pixels. While various change detection techniques have been proposed in the literature, the associated implementations usually do not scale well, which renders the corresponding analyses computationally very expensive or even impossible. In this work, we propose a novel massively-parallel implementation for a state-of-the-art change detection method and demonstrate its potential in the context of monitoring deforestation. The novel implementation can handle large scenarios in a few hours or days using cheap commodity hardware, compared to weeks or even years using the existing publicly available code, and enables researchers, for the first time, to conduct global-scale analyses covering large parts of our Earth using little computational resources. From a technical perspective, we provide a high-level parallel algorithm specification along with several performance-critical optimizations dedicated to efficiently map the specified parallelism to modern parallel devices. While a particular change detection method is addressed in this work, the algorithmic building blocks provided are also of immediate relevance to a wide variety of related approaches in remote sensing and other fields.

AB - Large amounts of satellite data are now becoming available, which, in combination with appropriate change detection methods, offer the opportunity to derive accurate information on timing and location of disturbances such as deforestation events across the earth surface. Typical scenarios require the analysis of billions of image patches/pixels. While various change detection techniques have been proposed in the literature, the associated implementations usually do not scale well, which renders the corresponding analyses computationally very expensive or even impossible. In this work, we propose a novel massively-parallel implementation for a state-of-the-art change detection method and demonstrate its potential in the context of monitoring deforestation. The novel implementation can handle large scenarios in a few hours or days using cheap commodity hardware, compared to weeks or even years using the existing publicly available code, and enables researchers, for the first time, to conduct global-scale analyses covering large parts of our Earth using little computational resources. From a technical perspective, we provide a high-level parallel algorithm specification along with several performance-critical optimizations dedicated to efficiently map the specified parallelism to modern parallel devices. While a particular change detection method is addressed in this work, the algorithmic building blocks provided are also of immediate relevance to a wide variety of related approaches in remote sensing and other fields.

UR - http://www.scopus.com/inward/record.url?scp=85085857297&partnerID=8YFLogxK

U2 - 10.1109/ICDE48307.2020.00040

DO - 10.1109/ICDE48307.2020.00040

M3 - Article in proceedings

AN - SCOPUS:85085857297

SP - 385

EP - 396

BT - Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020

PB - IEEE

T2 - 36th IEEE International Conference on Data Engineering, ICDE 2020

Y2 - 20 April 2020 through 24 April 2020

ER -