Distributed computing strategies in remote sensing techniques and applications that use various data sources are comprehensively reviewed. A new distributed fusion framework that can accelerate the fusion of heterogeneous remote sensing and social media data is proposed by decomposing large data sets into small ones and processing them in parallel. ABSTRACT | Despite the wide availability of remote sensing big data from numerous different Earth Observation (EO) instruments, the limitations in

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... e 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. 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).