Streaming Remote Sensing Data Processing for the Future Smart Cities: State of the Art and Future Challenges

Streaming Remote Sensing Data Processing for the Future Smart Cities: State of the Art and Future Challenges

Xihuang Sun (Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing, China), Peng Liu (Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing, China), Yan Ma (Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing, China), Dingsheng Liu (Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing, China) and Yechao Sun (China Centre for Resources Satellite Data and Application, Beijing, China)
Copyright: © 2016 |Pages: 14
DOI: 10.4018/IJDST.2016010101
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Abstract

The explosion of data and the increase in processing complexity, together with the increasing needs of real-time processing and concurrent data access, make remote sensing data streaming processing a wide research area to study. This paper introduces current situation of remote sensing data processing and how timely remote sensing data processing can help build future smart cities. Current research on remote sensing data streaming is also introduced where the three typical and open-source stream processing frameworks are introduced. This paper also discusses some design concerns for remote sensing data streaming processing systems, such as data model and transmission, system model, programming interfaces, storage management, availability, etc. Finally, this research specifically addresses some of the challenges of remote sensing data streaming processing, such as scalability, fault tolerance, consistency, load balancing and throughput.
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2.1. Current Works in RS Data Processing

To boost the speed of RS data processing, many research have been studied based on different architectures. These research mainly focus on offline RS data processing. Algorithms and processing patterns (Hailiang & LU, 2010; Jun-jun & Gang, 2010; Ke-fei, 2010; LIU et al., 2014) take the advantage of different hardware architectures such as GPU and FPGA. High performance clusters (Wei, Liu, & Wang, 2014; Xue-ping, 2013) enhance the RS data processing performance by using parallel computing. Distributed computing environments (Sekiguchi et al., 2008) can increase the data-storage capacity and expand the diversity of processing services.

There are also works based on cloud computing and RS big data computing. G-Hadoop (Wang et al., 2013) applied MapReduce across distributed data centers. Almeer(2012) employed Hadoop MapReduce framework to implement parallel processing of RS images and built an experimental 112-core high-performance cloud computing system. Wang (2014) estimates the statistical characteristics of RS big data in the wavelet transform domain. Task scheduling strategies (W. Zhang, Wang, Ma, & Liu, 2014) across data centers were also studied. Wang (2015) discusses some research issues about processing distributed internet of things data in clouds.

Apart from these works, Research on RS data streaming processing mainly focuses on data model and data querying. StreamInsight (Kazemitabar, Demiryurek, Ali, Akdogan, & Shahabi, 2010) support RS data querying by adding the Microsoft SQL server spatial library. Deng (2015) discusses the parallel processing of dynamic continuous queries over streaming data flows. Although system design is one of the most important areas in data streaming processing, there is still no mature data streaming processing system designed for RS data processing.

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