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A team of researchers from the University of Valencia (UV) has developed a new technological architecture to integrate environmental information from multiple sources, such as Earth observation satellites, ground-based sensors and drones.
The work, published in IEEE Geoscience and Remote Sensing Magazine, one of the most prestigious journals in the field of remote sensing and environmental data analysis, sets out a technical framework for organising large volumes of environmental data using semantic technologies, thereby facilitating interoperability and reuse in scientific studies and environmental applications.
The system has been developed and validated using the Valencia Anchor Station (VAS), a scientific facility located in the Utiel-Requena region that enables the combination of field measurements with data from various satellite missions such as Sentinel, Landsat, SMOS and MODIS.
The architecture developed, based on data semantics and ontologies (models that structure information using a common vocabulary to define the relationships between concepts), allows heterogeneous data to be integrated within a common semantic model. This, in turn, facilitates joint analysis and the conduct of environmental studies across different spatial and temporal scales.
The study, led by David García Rodríguez, a researcher in the Department of Computer Science and in the Institute for Research in Robotics and Information and Communication Technologies (IRTIC), both at the UV, also involves researchers from the Department of Earth Sciences and Thermodynamics and the Environmental Remote Sensing Group at the UV. This work forms part of the research line on semantic Earth observation based on artificial intelligence for crop sustainability and adaptation to climate change, led at IRTIC by Javier Samper Zapater and Juan José Martínez Durá.
Samper Zapater and Julián Gutiérrez Moret, IRTIC experts in data semantics and open data infrastructures, have made a significant contribution to the project, promoting an approach based on interoperability and open data spaces applied to Earth observation, as well as to the design and implementation of these key components.
Both have been instrumental in developing a cutting-edge framework that integrates artificial intelligence techniques with semantic models and data architectures, thereby establishing an innovative approach to the intelligent and sustainable management of agricultural systems in the context of climate change.
The results demonstrate the potential of semantic technologies to improve the management and exploitation of Earth observation data, with applications in environmental monitoring, precision agriculture, spatial planning and climate change studies. This work forms part of the R&D project PID2020-120438RBI00, funded by the Spanish Ministry of Science and Innovation and awarded to Juan José Martínez Durá.
Article reference: D. Garcia-Rodriguez et al., "Data Semantics for Earth Observation: A technical guide to ontology-based integration for environmental data monitoring," in IEEE Geoscience and Remote Sensing Magazine, doi: 10.1109/MGRS.2026.3667059
