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Scientific article
Open access
English

Towards semantic enrichment of Earth Observation data: The LEODS framework

Published inAGILE: GIScience series, vol. 5, p. 1-12
Publication date2024-05-30
First online date2024-05-30
Abstract

The Earth’s ecosystem is facing serious threats due to the depletion of natural resources and environmental pollution. To promote sustainable practices and formulate effective policies that address these issues, both experts and non-expert stakeholders require access to meaningful Open Data. Current Earth monitoring programs provide a large volume of open Earth Observation (EO) data typically organized and managed in EO Data Cubes (EODCs). From these datasets, satellite-derived indices can be calculated for assessing various environmental aspects in areas of interest over time. However, current EOs lack semantics and are isolated from significant Web resources, greatly hindering their comprehension and limiting their use to specialized users. To enhance EO data with semantic richness and ensure their understanding by a wider audience, it is pertinent to adopt a Linked Open Data (LOD) approach. In this paper, we present the Linked Earth Observation Data Series (LEODS) framework designed to publish aggregated EO data in the LOD Cloud. LEODS provides a processing chain that converts EO data into EO-RDF data cubes based on a spatio-temporal modeling approach that ensures integration and future semantic enrichment of EO data while preserving the advantages of traditional EODCs and following the FAIR principles (i.e., findable, accessible, interoperable, and reusable). To highlight the advantages of our proposal, we explore through SPARQL queries and visualizations, the results of implementing LEODS with study areas located in Switzerland and France.

eng
Keywords
  • Spatiotemporal data
  • Knowledge Graphs
  • Semantic Enrichment
  • Spatial Aggregation
  • Data Cube
  • Earth Observations
Citation (ISO format)
MILON-FLORES, Daniela F. et al. Towards semantic enrichment of Earth Observation data: The LEODS framework. In: AGILE: GIScience series, 2024, vol. 5, p. 1–12. doi: 10.5194/agile-giss-5-11-2024
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Article (Published version)
Identifiers
ISSN of the journal2700-8150
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