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.

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.