Ontology-based discovery of geographic information services—An application in disaster management

Klien, Eva; Lutz, Michael; Kuhn, Walther

doi:10.1016/j.compenvurbsys.2005.04.002

Cited by 130 publications

(84 citation statements)

References 18 publications

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“…This is useful for automatic finding and mining of data (Babitski et al, 2009;Klien et al, 2006). Guarino (1998) analyzed the importance of the ontology concept in GIS system.…”

Section: Related Work On Ontology In Disaster Data Managementmentioning

confidence: 99%

A Task-Driven Disaster Data Link Approach

Qiu

Zhu

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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“…This is useful for automatic finding and mining of data (Babitski et al, 2009;Klien et al, 2006). Guarino (1998) analyzed the importance of the ontology concept in GIS system.…”

Section: Related Work On Ontology In Disaster Data Managementmentioning

confidence: 99%

A Task-Driven Disaster Data Link Approach

Qiu

Zhu

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…This approach features high semantic integrity and formalizes metadata representation to make the metadata machine readable [29], which improve intercomprehension and interoperation among datasets [30,31].…”

Section: Semantic Description Of An Image Layermentioning

confidence: 99%

An On-Demand Retrieval Method Based on Hybrid NoSQL for Multi-Layer Image Tiles in Disaster Reduction Visualization

Qiu

Zhu

et al. 2017

IJGI

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Monitoring, response, mitigation and damage assessment of disasters places a wide variety of demands on the spatial and temporal resolutions of remote sensing images. Images are divided into tile pyramids by data sources or resolutions and published as independent image services for visualization. A disaster-affected area is commonly covered by multiple image layers to express hierarchical surface information, which generates a large amount of namesake tiles from different layers that overlay the same location. The traditional tile retrieval method for visualization cannot distinguish between distinct layers and traverses all image datasets for each tile query. This process produces redundant queries and invalid access that can seriously affect the visualization performance of clients, servers and network transmission. This paper proposes an on-demand retrieval method for multi-layer images and defines semantic annotations to enrich the description of each dataset. By matching visualization demands with the semantic information of datasets, this method automatically filters inappropriate layers and finds the most suitable layer for the final tile query. The design and implementation are based on a two-layer NoSQL database architecture that provides scheduling optimization and concurrent processing capability. The experimental results reflect the effectiveness and stability of the approach for multi-layer retrieval in disaster reduction visualization.

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“…Geographical information systems (GIS) are one of the most common applications that require intensive use of spatial properties are. GIS have been applying ontologies during the last years in a a wide variety of applications; e.g., disaster management [17], data retrieval [18], etc. Most of them are focused on representation issues, since they use ontologies to improve interoperability between heterogeneous systems.…”

Section: Related Workmentioning

confidence: 99%