Next Article in Journal
Temporal and Spatial Variations in the Leaf Area Index and Its Response to Topography in the Three-River Source Region, China from 2000 to 2017
Next Article in Special Issue
Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN
Previous Article in Journal
PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China
Previous Article in Special Issue
Determining Cover Management Factor with Remote Sensing and Spatial Analysis for Improving Long-Term Soil Loss Estimation in Watersheds
Article

Semantics-Driven Remote Sensing Scene Understanding Framework for Grounded Spatio-Contextual Scene Descriptions

Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2021, 10(1), 32; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010032
Received: 31 October 2020 / Revised: 17 December 2020 / Accepted: 11 January 2021 / Published: 13 January 2021
(This article belongs to the Special Issue Geospatial Artificial Intelligence)
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. View Full-Text
Keywords: remote sensing scene understanding; semantics-driven; grounded natural language scene descriptions; spatio-contextual; Scene Knowledge Graphs; flood ontology; semantic web; GeoSPARQL; Resource Description Framework (RDF); Semantic Web Rule Language (SWRL) remote sensing scene understanding; semantics-driven; grounded natural language scene descriptions; spatio-contextual; Scene Knowledge Graphs; flood ontology; semantic web; GeoSPARQL; Resource Description Framework (RDF); Semantic Web Rule Language (SWRL)
Show Figures

Figure 1

MDPI and ACS Style

Potnis, A.V.; Durbha, S.S.; Shinde, R.C. Semantics-Driven Remote Sensing Scene Understanding Framework for Grounded Spatio-Contextual Scene Descriptions. ISPRS Int. J. Geo-Inf. 2021, 10, 32. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010032

AMA Style

Potnis AV, Durbha SS, Shinde RC. Semantics-Driven Remote Sensing Scene Understanding Framework for Grounded Spatio-Contextual Scene Descriptions. ISPRS International Journal of Geo-Information. 2021; 10(1):32. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010032

Chicago/Turabian Style

Potnis, Abhishek V., Surya S. Durbha, and Rajat C. Shinde 2021. "Semantics-Driven Remote Sensing Scene Understanding Framework for Grounded Spatio-Contextual Scene Descriptions" ISPRS International Journal of Geo-Information 10, no. 1: 32. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010032

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop