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Corporate InformationResearch & Development

January 10, 2017

Report from Presenter

Fig. 1 R&D project on G-space platform

GSDI (Global Spatial Data Infrastructure Association) 15th World Conference was held at Taipei Nangang Exhibition Center, Taipei, Taiwan from Nov. 28th 2016 to Dec. 2th 2016. The theme of this conference is spatial enablement in smart homeland, including three main topics of smart disaster prevention, smart transportation and smart city. In this main conference, there were 12 keynote speeches and 109 oral presentations. In these oral presentations, the author presented our research relevant to verification of real-time use and application techniques about dynamic geospatial information in disaster management.

Recently, large quantities of data (dynamic geospatial information) observed by smartphones and sensor networks make a society resilient to disasters and create new services. For enabling real-time use and application of dynamic geospatial information, Hitachi Ltd., The University of Tokyo, KDDI CORPORATION, and KDDI Research, Inc developed "real-time processing technique", "high-speed spatio-temporal search technique", and "integrated analysis techniques of multiple types of geospatial data" (Fig. 1). Additionally, they considered a use case of estimating damages in a large-scale disaster and conducted a demonstration experiment to verify the effectiveness of these developed techniques in disaster management. The results of this experiment were presented at this conference as "Are estimation algorithms applicable for disaster management? - Experimental demonstration of disaster-information-integration platform named 'G-space platform' ".

Fig. 2 Estimation and display of fire spread coverage

Fig. 3 Estimation and display of population distribution

Fig. 4 Estimation and display of survivor distribution

When a large-scale disaster occurs, damage information is collected in the first action period. After that, disaster-relief operations and support over a wide area depending on the scale of the disaster are requested. In this situation, it is important to reduce the time taken to understand damage situations. The authors considered a scenario in which the developed techniques estimate damage situations based on insufficient observed data collected in a large-scale disaster, and then implemented a demonstration system. Specifically the authors considered a disaster scenario of fire spreading caused by a large-scale Tokyo Inland Earthquake and assumed to collect insufficient disaster information and mobile communication logs in this disaster. This demonstration system estimated fire spread coverage (Fig. 2), population distribution (Fig. 3), and survivor distribution (Fig. 4) and visualize these estimated results on a map. In an experimental demonstration held from Jan. 27th 2016 to Jan. 28th 2016, the authors showed the demonstration system to experts and government officers in the field of ICT or disaster management. Additionally the authors conducted discussions with them and questionnaires to them. From these results, the authors confirmed the effectiveness for the developed techniques to understand damage situations in the large-scale disaster.

In the future, the authors plan to deploy the research and development results to many kinds of field including disaster management for practical applications.


This work is supported by consignment research and development of techniques about use and application of real-time information in G-space platform from the Ministry of Internal Affairs and Communications, Japan.

(By HAYASHI Hideki)

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