Services for flood disaster loss evaluation based on remote sensing and grid

Jun Zhu; Jianhua Gong; Hui Lin; Daojun Wang; Bingli Xu

doi:10.1117/12.651851

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2 December 2005 Services for flood disaster loss evaluation based on remote sensing and grid

Jun Zhu, Jianhua Gong, Hui Lin, Daojun Wang, Bingli Xu

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Proceedings Volume 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications; 604536 (2005) https://doi.org/10.1117/12.651851
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

Abstract

Flood disaster loss evaluation (FDLE) plays a very important role in Flood Control Decision Support System (FCDSS). In recent years, there are more and more spatial data, especially Remote Sensing (RS) images data. Meantime, the updating ratio of RS images data is faster too. So in this paper we focus on flood disaster loss evaluation model based on RS images. It is very difficult to get value information in real time because all RS data resources are geographically distributed and heterogeneous, and these data update is also not rapid through traditional data service mode such as data purchase. Grid technology can provide us the capability to solve these issues effectively. In this paper, we firstly address the major characteristics of RS and Grid technologies, and analyze the possibility for FDLE services. Based on Grid technology, then we design the FDLE service system architecture and its implementing mechanism is addressed. Meantime, some key technologies such as data management and services management also are discussed. Finally, we implement an application service. All these efforts will help us integrate all diverse and separated data resources and offer more convenient and rapid FDLE information services. The experiment result shows that the scheme addressed in the paper is efficient and feasible.

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Jun Zhu, Jianhua Gong, Hui Lin, Daojun Wang, and Bingli Xu "Services for flood disaster loss evaluation based on remote sensing and grid", Proc. SPIE 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications, 604536 (2 December 2005); https://doi.org/10.1117/12.651851

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