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Sensors 2017, 17(11), 2545; doi:10.3390/s17112545

An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring

Center for Applied Autonomous Sensor Systems, Örebro University, 702 81 Örebro, Sweden
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Received: 20 September 2017 / Revised: 20 October 2017 / Accepted: 2 November 2017 / Published: 5 November 2017
(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)
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Abstract

This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints. View Full-Text
Keywords: satellite imagery data; natural hazards; ontology; reasoning; path finding satellite imagery data; natural hazards; ontology; reasoning; path finding
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MDPI and ACS Style

Alirezaie, M.; Kiselev, A.; Längkvist, M.; Klügl, F.; Loutfi, A. An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring. Sensors 2017, 17, 2545.

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