Abstract
Earth Observation data possess tremendous potential in understanding the dynamics of our planet. We propose the Semantics-driven Remote Sensing Scene Understanding (Sem-RSSU) framework for rendering comprehensive grounded spatio-contextual scene descriptions for enhanced situational awareness. To minimize the semantic gap for remote-sensing-scene understanding, the framework puts forward the transformation of scenes by using semantic-web technologies to Remote Sensing Scene Knowledge Graphs (RSS-KGs). The knowledge-graph representation of scenes has been formalized through the development of a Remote Sensing Scene Ontology (RSSO)—a core ontology for an inclusive remote-sensing-scene data product. The RSS-KGs are enriched both spatially and contextually, using a deductive reasoner, by mining for implicit spatio-contextual relationships between land-cover classes in the scenes. The Sem-RSSU, at its core, constitutes novel Ontology-driven Spatio-Contextual Triple Aggregation and realization algorithms to transform KGs to render grounded natural language scene descriptions. Considering the significance of scene understanding for informed decision-making from remote sensing scenes during a flood, we selected it as a test scenario, to demonstrate the utility of this framework. In that regard, a contextual domain knowledge encompassing Flood Scene Ontology (FSO) has been developed. Extensive experimental evaluations show promising results, further validating the efficacy of this framework.
Original language | English |
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Article number | 32 |
Journal | ISPRS International Journal of Geo-Information |
Volume | 10 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2021 |
Externally published | Yes |
Funding
The authors are grateful to the Google Cloud Research Credits Program for providing the credits that enabled the use of GCP for training and validating the deep-learning models.
Funders | Funder number |
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Keywords
- Flood ontology
- GeoSPARQL
- Grounded natural language scene descriptions
- Remote sensing scene understanding
- Resource Description Framework (RDF)
- Scene Knowledge Graphs
- Semantic Web Rule Language (SWRL)
- Semantic web
- Semantics-driven
- Spatio-contextual