Abstract
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, case data, simulated data, and disaster products. However, the efficiency of current data management and service systems has become increasingly difficult due to the task variety and heterogeneous data. For emergency task-oriented applications, the data searches primarily rely on artificial experience based on simple metadata indices, the high time consumption and low accuracy of which cannot satisfy the speed and veracity requirements for disaster products. In this paper, a task-oriented correlation method is proposed for efficient disaster data management and intelligent service with the objectives of 1) putting forward disaster task ontology and data ontology to unify the different semantics of multi-source information, 2) identifying the semantic mapping from emergency tasks to multiple data sources on the basis of uniform description in 1), and 3) linking task-related data automatically and calculating the correlation between each data set and a certain task. The method goes beyond traditional static management of disaster data and establishes a basis for intelligent retrieval and active dissemination of disaster information. The case study presented in this paper illustrates the use of the method on an example flood emergency relief task.
Highlights
The incidence and magnitude of natural disasters worldwide have increased significantly due to climate change in recent years (Ding et al, 2014; Iwata et al, 2014; Neumayer et al, 2014)
The functions and purposes of disaster information services are focused and clear, which could rapidly satisfy specific user needs, but these systems will not fit the needs of the actual disaster management tasks of other user communities and not generate products with high accuracy and veracity when the required data source is limited or not accessible
2) Current efforts to integrate geographic information data have been restricted to keyword-based matching Spatial Information Infrastructure (SII) (Li et al, 2007)
Summary
The incidence and magnitude of natural disasters worldwide have increased significantly due to climate change in recent years (Ding et al, 2014; Iwata et al, 2014; Neumayer et al, 2014). Various types of sensors widely deployed in disaster monitoring networks make it possible to continuously access large disaster datasets with high spatial-temporal resolution and increasingly rich attribute information, which provides important support for enhancing capabilities of disaster emergency responses. The section titled “Task and data ontologies for disaster management” firstly analyzes types and features of emergency tasks in disaster management and puts forward an ontology model describing them. It describes the semantic features of disaster data in regards to attribute, space-time and statistics. We conclude the article in the “Conclusions and future work” section
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