SHAHED: A MapReduce-based System for Querying and Visualizing Spatio-temporal Satellite Data
Remote sensing data collected by satellites are publicly available through several space agencies. This data is used by scientists pursuing research in climate change, desertification, and land use change. Unfortunately, the inefficiencies of traditional analysis applications in querying such large archives (> 500TB) limit the use of such data.
SHAHED is a MapReduce based system for querying, visualizing, and mining large scale satellite data. SHAHED considers both the spatial and temporal aspects of the data to provide efficient query processing at scale. SHAHED is composed of four main components. The uncertainty component recovers missing data in the input which comes from cloud coverage and satellite misalignment. The indexing component provides a novel multi-resolution quad-tree-based spatio-temporal index structure, which indexes satellite data efficiently with minimal space overhead. The querying component answers selection and aggregate queries in real-time using the constructed index. Finally, the visualization component uses MapReduce programs to generate images and videos for user queries.