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Poster
Abstract:
For more than 40 years, remote sensing satellite missions are globally scanning the earth´s surface. They are ideal instruments for monitoring spatio-temporal changes. A comprehensive analysis of this data has the potential to support solutions to major global change challenges related to climate change, population growth, water scarcity, or loss of biodiversity. However, a comprehensive analysis of these remote sensing data is a challenging task: (a) there is a lack of Big Data-adapted analysis tools, (b) the number of available sensors will steadily increase over the next years and (c) technological advancements allow to measure data at higher spatial, spectral, and temporal resolutions than ever before. These developments create an urgent need to better analyze huge and heterogeneous data volumes in support of e.g. global change research.
The interdisciplinary research consortium of the “GeoMultiSens” project focuses on developing an open source, scalable and modular Big Data system that combines data from different sensors and analyzes data in the petabyte range (1015 Byte). The most important modules are: (1) data acquisition, (2) pre-processing and homogenization, (3) storage, (4) analysis, and (5) visual exploration. The data acquisition module enables users to specify a region and time interval of interest, to identify the available remote sensing scenes in different data archives, to assess how these scenes are distributed in space and time, and to decide which scenes to use for a specific analysis. The homogenization module uses novel and state-of-the-art algorithms that combine the selected remote sensing scenes from different sensors into a common data set. The data storage module optimises storage and processing of petabytes of data in a parallel and failure tolerant manner. The core technology of the data storage module is XtreemFS (http://www.xtreemfs.org). The analysis module implements image classification and time series analysis algorithms. The visual exploration module supports users in assessing the analysis results. All modules are adapted to a map-reduce processing scheme to allow a very fast information retrieval and parallel computing within the processing system Flink (http://flink.apache.org). Finally, a Visual Analytics approach integrates the individual modules and provides a visual interface to each step in the analysis pipeline.
The Big Data system “GeoMultiSens” will store and process remotely sensed data from space-borne multispectral sensors of high and medium spatial resolution such as Sentinel-2, Landsat 5/7/8, Spot 1-6, ASTER, ALOS AVNIR-2 and RapidEye. Our poster presents the overall scientific concept of the Big Data system “GeoMultiSens” and technical details of the most important modules. We discuss scientific challenges of the Big Data system “GeoMultiSens” and present our ideas to address these scientific challenges.
