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A large volume of remote sensing (RS) data has been generated with thedeployment of satellite technologies. The data facilitates research inecological monitoring, land management and desertification, etc. Thecharacteristics of RS data (e.g., enormous volume, large single-file size anddemanding requirement of fault tolerance) make the Hadoop Distributed FileSystem (HDFS) an ideal choice for RS data storage as it is efficient, scalableand equipped with a data replication mechanism for failure resilience. To useRS data, one of the most important techniques is geospatial indexing. However,the large data volume makes it time-consuming to efficiently construct andleverage. Considering that most modern geospatial data centres are equippedwith HDFS-based big data processing infrastructures, deploying multiplegeospatial indices becomes natural to optimise the efficacy. Moreover, becauseof the reliability introduced by high-quality hardware and the infrequentlymodified property of the RS data, the use of multi-indexing will not causelarge overhead. Therefore, we design a framework called Multi-IndeXing-RS(MIX-RS) that unifies the multi-indexing mechanism on top of the HDFS with datareplication enabled for both fault tolerance and geospatial indexingefficiency. Given the fault tolerance provided by the HDFS, RS data isstructurally stored inside for faster geospatial indexing. Additionally,multi-indexing enhances efficiency. The proposed technique naturally sits ontop of the HDFS to form a holistic framework without incurring severe overheador sophisticated system implementation efforts. The MIX-RS framework isimplemented and evaluated using real remote sensing data provided by theChinese Academy of Sciences, demonstrating excellent geospatial indexingperformance.

MIX-RS: A Multi-indexing System based on HDFS for Remote Sensing Data Storage