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Article

RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System

1
Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editor: Kohei Arai
Remote Sens. 2021, 13(9), 1815; https://doi.org/10.3390/rs13091815
Received: 11 April 2021 / Revised: 25 April 2021 / Accepted: 29 April 2021 / Published: 6 May 2021
With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it’s still a big challenge to manage and process such huge amounts of remote sensing data with complex and diverse structures. This paper designs and realizes a distributed storage system for large-scale remote sensing data storage, access, and retrieval, called RSIMS (remote sensing images management system), which is composed of three sub-modules: RSIAPI, RSIMeta, RSIData. Structured text metadata of different remote sensing images are all stored in RSIMeta based on a set of uniform models, and then indexed by the distributed multi-level Hilbert grids for high spatiotemporal retrieval performance. Unstructured binary image files are stored in RSIData, which provides large scalable storage capacity and efficient GDAL (Geospatial Data Abstraction Library) compatible I/O interfaces. Popular GIS software and tools (e.g., QGIS, ArcGIS, rasterio) can access data stored in RSIData directly. RSIAPI provides users a set of uniform interfaces for data access and retrieval, hiding the complex inner structures of RSIMS. The test results show that RSIMS can store and manage large amounts of remote sensing images from various sources with high and stable performance, and is easy to deploy and use. View Full-Text
Keywords: remote sensing; big data; distributed storage system; distributed spatial index; Hilbert remote sensing; big data; distributed storage system; distributed spatial index; Hilbert
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MDPI and ACS Style

Zhou, X.; Wang, X.; Zhou, Y.; Lin, Q.; Zhao, J.; Meng, X. RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System. Remote Sens. 2021, 13, 1815. https://doi.org/10.3390/rs13091815

AMA Style

Zhou X, Wang X, Zhou Y, Lin Q, Zhao J, Meng X. RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System. Remote Sensing. 2021; 13(9):1815. https://doi.org/10.3390/rs13091815

Chicago/Turabian Style

Zhou, Xiaohua, Xuezhi Wang, Yuanchun Zhou, Qinghui Lin, Jianghua Zhao, and Xianghai Meng. 2021. "RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System" Remote Sensing 13, no. 9: 1815. https://doi.org/10.3390/rs13091815

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