An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring
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 qualit...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-11-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/17/11/2545 |
| _version_ | 1811263044937842688 |
|---|---|
| author | Marjan Alirezaie Andrey Kiselev Martin Längkvist Franziska Klügl Amy Loutfi |
| author_facet | Marjan Alirezaie Andrey Kiselev Martin Längkvist Franziska Klügl Amy Loutfi |
| author_sort | Marjan Alirezaie |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T19:37:54Z |
| format | Article |
| id | doaj.art-8ea7d95acdee43959cc6071b80a52da5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-12T19:37:54Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8ea7d95acdee43959cc6071b80a52da52022-12-22T03:19:10ZengMDPI AGSensors1424-82202017-11-011711254510.3390/s17112545s17112545An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster MonitoringMarjan Alirezaie0Andrey Kiselev1Martin Längkvist2Franziska Klügl3Amy Loutfi4Center for Applied Autonomous Sensor Systems, Örebro University, 702 81 Örebro, SwedenCenter for Applied Autonomous Sensor Systems, Örebro University, 702 81 Örebro, SwedenCenter for Applied Autonomous Sensor Systems, Örebro University, 702 81 Örebro, SwedenCenter for Applied Autonomous Sensor Systems, Örebro University, 702 81 Örebro, SwedenCenter for Applied Autonomous Sensor Systems, Örebro University, 702 81 Örebro, SwedenThis 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.https://www.mdpi.com/1424-8220/17/11/2545satellite imagery datanatural hazardsontologyreasoningpath finding |
| spellingShingle | Marjan Alirezaie Andrey Kiselev Martin Längkvist Franziska Klügl Amy Loutfi An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring Sensors satellite imagery data natural hazards ontology reasoning path finding |
| title | An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring |
| title_full | An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring |
| title_fullStr | An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring |
| title_full_unstemmed | An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring |
| title_short | An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring |
| title_sort | ontology based reasoning framework for querying satellite images for disaster monitoring |
| topic | satellite imagery data natural hazards ontology reasoning path finding |
| url | https://www.mdpi.com/1424-8220/17/11/2545 |
| work_keys_str_mv | AT marjanalirezaie anontologybasedreasoningframeworkforqueryingsatelliteimagesfordisastermonitoring AT andreykiselev anontologybasedreasoningframeworkforqueryingsatelliteimagesfordisastermonitoring AT martinlangkvist anontologybasedreasoningframeworkforqueryingsatelliteimagesfordisastermonitoring AT franziskaklugl anontologybasedreasoningframeworkforqueryingsatelliteimagesfordisastermonitoring AT amyloutfi anontologybasedreasoningframeworkforqueryingsatelliteimagesfordisastermonitoring AT marjanalirezaie ontologybasedreasoningframeworkforqueryingsatelliteimagesfordisastermonitoring AT andreykiselev ontologybasedreasoningframeworkforqueryingsatelliteimagesfordisastermonitoring AT martinlangkvist ontologybasedreasoningframeworkforqueryingsatelliteimagesfordisastermonitoring AT franziskaklugl ontologybasedreasoningframeworkforqueryingsatelliteimagesfordisastermonitoring AT amyloutfi ontologybasedreasoningframeworkforqueryingsatelliteimagesfordisastermonitoring |