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ScienceEarth: A Big Data Platform for Remote Sensing Data Processing

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
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Remote Sens. 2020, 12(4), 607; https://doi.org/10.3390/rs12040607
Received: 19 January 2020 / Revised: 5 February 2020 / Accepted: 10 February 2020 / Published: 12 February 2020
Mass remote sensing data management and processing is currently one of the most important topics. In this study, we introduce ScienceEarth, a cluster-based data processing framework. The aim of ScienceEarth is to store, manage, and process large-scale remote sensing data in a cloud-based cluster-computing environment. The platform consists of the following three main parts: ScienceGeoData, ScienceGeoIndex, and ScienceGeoSpark. ScienceGeoData stores and manages remote sensing data. ScienceGeoIndex is an index and query system, a spatial index based on quad-tree and Hilbert curve which is combined for heterogeneous tiled remote sensing data that makes efficient data retrieval in ScienceGeoData. ScienceGeoSpark is an easy-to-use computing framework in which we use Apache Spark as the analytics engine for big remote sensing data processing. The result of tests proves that ScienceEarth can efficiently store, retrieve, and process remote sensing data. The results reveal ScienceEarth has the potential and capabilities of efficient big remote sensing data processing. View Full-Text
Keywords: big data; remote sensing data processing; distributed file system; HBase; Spark big data; remote sensing data processing; distributed file system; HBase; Spark
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MDPI and ACS Style

Xu, C.; Du, X.; Yan, Z.; Fan, X. ScienceEarth: A Big Data Platform for Remote Sensing Data Processing. Remote Sens. 2020, 12, 607.

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