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An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring
Örebro University, School of Science and Technology. (Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-4001-2087
Örebro University, School of Science and Technology. (Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-0305-3728
Örebro University, School of Science and Technology. (Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-0579-7181
Örebro University, School of Science and Technology. (Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-1470-6288
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2017 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 11, article id 2545Article in journal, Editorial material (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
M D P I AG , 2017. Vol. 17, no 11, article id 2545
Keyword [en]
satellite imagery data; natural hazards; ontology; reasoning; path finding
National Category
Computer Systems
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-62134DOI: 10.3390/s17112545ISI: 000416790500107PubMedID: 29113073Scopus ID: 2-s2.0-85033372857OAI: oai:DiVA.org:oru-62134DiVA, id: diva2:1154798
Projects
Semantic Robot
Available from: 2017-11-05 Created: 2017-11-05 Last updated: 2018-01-03Bibliographically approved

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Alirezaie, MarjanKiselev, AndreyLängkvist, MartinKlügl, FranziskaLoutfi, Amy
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