MIX-RS: A Multi-indexing System based on HDFS for Remote Sensing Data Storage
Jiashu Wu, Jingpan Xiong, Hao Dai, Yang Wang, Chengzhong Xu

TL;DR
MIX-RS is a multi-indexing framework built on HDFS that improves geospatial data retrieval efficiency for large remote sensing datasets, ensuring fault tolerance and scalability.
Contribution
The paper introduces MIX-RS, a novel multi-indexing system on HDFS that enhances geospatial data access for remote sensing applications with minimal overhead.
Findings
Achieves faster geospatial indexing performance on real RS data.
Provides fault tolerance through HDFS data replication.
Efficiently handles large-scale remote sensing datasets.
Abstract
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,…
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