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From EO Change Rasters to Knowledge Graphs: An approach Based on Regions of Interest

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Abstract

This paper proposes a process that supports the full life-cycle to generate and exploit knowledge graphs (KG) from Earth observation rasters and open data. The innovative features of this process include i) an algorithm to automatically identify Regions of Interest (ROIs) on a raster, offering an accurate geographic division, i.e. geolocated references used to guide data integration; ii) a semantic-driven process to generate a KG from different sources (raster, open, linked and social data); and iii) a validation of the approach with a use case on fire detection, which shows the added value of KGs to identify ROIs where high changes have been detected.
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Dates and versions

hal-03327705 , version 1 (27-08-2021)

Licence

Attribution - CC BY 4.0

Identifiers

  • HAL Id : hal-03327705 , version 1

Cite

Jordane Dorne, Nathalie Aussenac-Gilles, Catherine Comparot, Romain Hugues, Cassia Trojahn. From EO Change Rasters to Knowledge Graphs: An approach Based on Regions of Interest. 4th International Workshop on Geospatial Linked Data (GeoLD 2021@ESWC 2021), Beyza Yaman; Mohamed Ahmed Sherif; Axel-Cyrille Ngonga Ngomo; Armin Haller, Jun 2021, Hersonissos, Greece. pp.67-79. ⟨hal-03327705⟩
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