Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

作     者：Li, Jun Liu, Zhenjie Lei, Xinya Wang, Lizhe 

作者机构：Sun Yat Sen Univ Sch Geog & Planning Guangdong Prov Key Lab Urbanizat & Geosimulat Guangzhou 510275 Peoples R China China Univ Geosci Wuhan Sch Comp Sci Wuhan 430074 Peoples R China 

出 版 物：《PROCEEDINGS OF THE IEEE》 (电气与电子工程师学会会报)

年 卷 期：2021年第109卷第8期

页      面：1350-1363页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19090104] National Natural Science Foundation of China [42030111, 61771496] National Key Research and Development Program of China [2017YFB0502900] 

主　　题：Remote sensing Distributed databases Earth Artificial satellites Monitoring MODIS Temperature sensors Data fusion parallel and distributed computing remote sensing social media 

摘      要：Despite the wide availability of remote sensing big data from numerous different Earth Observation (EO) instruments, the limitations in the spatial and temporal resolution of such EO sensors (as well as atmospheric opacity and other kinds of interferers) have led to many situations in which using only remote sensing data cannot fully meet the requirements of applications in which a (near) real-time response is needed. Examples of these applications include floods, earthquakes, and other kinds of natural disasters, such as typhoons. To address this issue, social media data have gradually been adopted to fill possible gaps in the analysis when remote sensing data are lacking or incomplete. In this case, the fusion of heterogeneous big data streams from multiple data sources introduces significant demands from a computational viewpoint. In order to meet these challenges, distributed computing is increasingly viewed as a feasible solution to parallelize the analysis of massive data coming from different sources (e.g., remote sensing and social media data). In this article, we provide an overview of available and new distributed strategies to address the computational challenges brought by massive heterogeneous data processing and fusion for real-time environmental monitoring and decision-making. The 2013 Boulder (Colorado) flood event is taken as a case study to evaluate several new distributed data fusion frameworks. Experimental results demonstrate that the proposed distributed frameworks are suitable in terms of response time and computational requirements for fusing large-volume heterogeneous data sources.