Volume XL-3/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 179-186, 2015
https://doi.org/10.5194/isprsarchives-XL-3-W3-179-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 179-186, 2015
https://doi.org/10.5194/isprsarchives-XL-3-W3-179-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  19 Aug 2015

19 Aug 2015

A TASK-DRIVEN DISASTER DATA LINK APPROACH

L. Y. Qiu1, Q. Zhu2,3, J. Y. Gu1, and Z. Q. Du1,4 L. Y. Qiu et al.
  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430079 Wuhan, China
  • 2State-province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety, Southwest Jiaotong University, 610000, Chengdu, China
  • 3Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, 610000, Chengdu, China
  • 4Collaborative Innovation Center of Geospatial Technology, 430079 Wuhan, China

Keywords: Emergency Task, Ontology, Disaster Data Management, Semantic Mapping, Spatial-temporal Correlation, Data Link

Abstract. With the rapid development of sensor networks and Earth observation technology, a large quantity of disaster-related data is available, such as remotely sensed data, historic data, cases data, simulation data, disaster products and so on. However, the efficiency of current data management and service systems has become increasingly serious due to the task variety and heterogeneous data. For emergency task-oriented applications, data searching mainly relies on artificial experience based on simple metadata index, whose high time-consuming and low accuracy cannot satisfy the requirements of disaster products on velocity and veracity. In this paper, a task-oriented linking method is proposed for efficient disaster data management and intelligent service, with the objectives of 1) putting forward ontologies of disaster task and data to unify the different semantics of multi-source information, 2) identifying the semantic mapping from emergency tasks to multiple sources on the basis of uniform description in 1), 3) linking task-related data automatically and calculating the degree of correlation between each data and a target task. The method breaks through traditional static management of disaster data and establishes a base for intelligent retrieval and active push of disaster information. The case study presented in this paper illustrates the use of the method with a flood emergency relief task.