  A large volume of remote sensing (RS) data has been generated with the
deployment of satellite technologies. The data facilitates research in
ecological monitoring, land management and desertification, etc. The
characteristics of RS data (e.g., enormous volume, large single-file size and
demanding requirement of fault tolerance) make the Hadoop Distributed File
System (HDFS) an ideal choice for RS data storage as it is efficient, scalable
and equipped with a data replication mechanism for failure resilience. To use
RS data, one of the most important techniques is geospatial indexing. However,
the large data volume makes it time-consuming to efficiently construct and
leverage. Considering that most modern geospatial data centres are equipped
with HDFS-based big data processing infrastructures, deploying multiple
geospatial indices becomes natural to optimise the efficacy. Moreover, because
of the reliability introduced by high-quality hardware and the infrequently
modified property of the RS data, the use of multi-indexing will not cause
large overhead. Therefore, we design a framework called Multi-IndeXing-RS
(MIX-RS) that unifies the multi-indexing mechanism on top of the HDFS with data
replication enabled for both fault tolerance and geospatial indexing
efficiency. Given the fault tolerance provided by the HDFS, RS data is
structurally stored inside for faster geospatial indexing. Additionally,
multi-indexing enhances efficiency. The proposed technique naturally sits on
top of the HDFS to form a holistic framework without incurring severe overhead
or sophisticated system implementation efforts. The MIX-RS framework is
implemented and evaluated using real remote sensing data provided by the
Chinese Academy of Sciences, demonstrating excellent geospatial indexing
performance.
