Abstract
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.
Highlights
Due to the limitations in the spatial and temporal resolutions of Earth Observation (EO) instruments, there is a need to integrate remote sensing data with other sources for EO applications, especially in the area of emergency response [1]
We explore the feasibility of developing distributed parallel methods to address the computationally challenging problem of fusing heterogeneous data sources based on domain adaptation, providing an example of the distributed fusion of remote sensing and social media data during floods
We introduce some of the most important related works that are based on fusing remote sensing and other sources of information
Summary
Due to the limitations in the spatial and temporal resolutions of Earth Observation (EO) instruments, there is a need to integrate remote sensing data with other sources for EO applications, especially in the area of emergency response [1]. Li et al.: Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data adaptation techniques [3]) are computationally expensive, leading to the fact that the associated time constraints limit their applicability (especially, in the context of emergency response applications [4], [5]). We focus on distributed computing techniques for the fusion of remote sensing data and social media data. We explore the feasibility of developing distributed parallel methods to address the computationally challenging problem of fusing heterogeneous data sources based on domain adaptation, providing an example of the distributed fusion of remote sensing and social media data during floods. 2) Based on our review of parallel and distributed techniques for fusing heterogeneous big data in remote sensing, new distributed fusion frameworks are proposed.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
