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, 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.
Highlights
The number of incidents and magnitude of natural disasters worldwide have increased significantly due to climate changes in recent years (Ding et al, 2014; Iwata et al, 2014; Neumayer et al, 2014)
2) Current efforts to integrate geographic information data have been restricted to keyword-basedmatching Spatial Information Infrastructure (SII) (Li et al, 2007)
This paper proposes a task-oriented disaster information link method, in which disaster emergency tasks are regarded as a key semantic factor to restrain, associate and aggregate spatial-temporal data
Summary
The number of incidents and magnitude of natural disasters worldwide have increased significantly due to climate changes in recent years (Ding et al, 2014; Iwata et al, 2014; Neumayer et al, 2014). Disaster data, including remote sensing images, history data, previous incidents records, simulation data, basic geographic data and disaster assessment products possess velocity, variety and veracity features converted from singleness and small amount They put forward a higher requirement for integration, processing and analysis (Grolinger et al, 2013). 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 “Conclusions and future work” section
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