The significance of Linked Open Data datasets for traffic information extends beyond just including open traffic data. It incorporates links to other relevant thematic datasets available on the web. This enables federated queries across different data platforms from various countries and sectors, such as transport, geospatial, environmental, weather, and more. Businesses, researchers, national operators, administrators, and citizens at large can benefit from having dynamic traffic open data connected to heterogeneous datasets across Member States. This paper focuses on the development of a semantic model that enhances the basic service to access open traffic data through a LOD-enhanced Traffic Information System in alignment with the ITS Directive (2010/40/EU). The objective is not limited to just viewing or downloading data but also to improve the extraction of meaningful information and enable other types of services that are only achievable through LOD. By structuring the information using the RDF format meant for machines and employing SPARQL for querying, LOD allows for comprehensive and unified access to all datasets. Considering that the European standard DATEX II is widely used in many priority areas and services mentioned in the ITS Directive, LOD DATEX II was developed as a complementary approach to DATEX II XML. This facilitates the accessibility and comprehensibility of European traffic data and services. As part of this development, an ontological model called dtx_srti, based on the DATEX II Ontology, was created to support these efforts.

The integration of semantic technologies and geospatial information, especially when introducing information from Earth observation (EO) satellites, is significantly transforming methods for monitoring and diagnosing the environment and its most critical processes. This integration provides robust, cutting-edge analytical methods, thanks to the availability of more abundant and affordable data, which improves decision-making processes. This research proposes a technical tutorial guide for the application of an ontology system architecture built on semantic ontologies for remote sensing (RS) parameters, ground-based reference measurements, and observations acquired from uncrewed aerial vehicles (UAVs), enabling their joint use by scientists in the field. Although the work was ­originally developed using the Valencia Anchor Station (VAS) database, the system architecture ensures high adaptability and scalability, making it suitable for deployment across diverse station types and for application in other study areas. Through the application of linked open data (LOD) principles, the VAS ontology model integrates 1) EO-based data from Copernicus Sentinel-1, -2, and -3 satellites and other missions such as SMOS (Soil Moisture and Ocean Salinity), SMAP (Soil Moisture Active Passive), Landsat-8, -9, Terra and Aqua MODIS (Moderate Resolution Imaging Spectroradiometer), and so on; 2) observations from UAVs; 3) in situ data from sensors networks; and 4) auxiliary information from land cover classes, administrative units, and other features of interest to enhance environmental monitoring. The ontology is designed to represent the relationships between these elements, enabling systematic organization and retrieval of environmental data. The methodology is highly adaptable and can be extended to a wide range of applications, including validation of satellite RS products, precision agriculture, urban planning or disaster management. The potential of this approach to unify diverse data sources and streamline complex analytical processes is highly significant. Given the large number of ontologies capable of handling large volumes of data, the VAS ontology is presented as an illustrative example of their potential. Among the large number of parameters of all kinds involved, as an example application, this article refers specifically to the analysis of changes in the fraction of absorbed photosynthetically active radiation (FAPAR) and related vegetation characteristics at the VAS for the period 2018–2023. The proposed ontology has been tested in the Utiel-Requena region (Valencia, Spain), serving as a pilot area to validate the effectiveness and applicability of the architecture of the proposed approach. Fusion analytical data layers use a centralized data architecture, which supports real-time visualization and the capability of analyzing temporal trends and environmental variability. Validation tests assess the correctness and strength of the model, providing simple access to structured environmental data via an REST (Representational State Transfer) application programming interface (API) built using Spring Boot. The findings demonstrate the effectiveness of field, UAV, and satellite data fusion for multiscale analysis (spatial, temporal, and spectral), offering a potential and versatile tool for the integration of diverse data sources in a wide range of applications within the EO community.

The significance of Linked Open Data datasets for traffic information goes beyond just including open traffic data. It extends to incorporating links to other relevant thematic datasets available on the web. This enables federated queries across different data platforms from various countries and sectors, such as transport, geospatial, environmental, weather, and more. Businesses, researchers, national operators, administrators, and citizens at large can benefit from having dynamic traffic open data connected to heterogeneous datasets across different Member States. This paper focuses on the development of a semantic model that enhances the basic service of accessing open traffic data through a LOD-enhanced Traffic Information System in alignment with the ITS Directive (2010/40/EU). The objective is not limited to just viewing or downloading data but also to improve the extraction of meaningful information and enable other types of services that are only achievable through LOD. By structuring the information using the RDF format meant for machines and employing SPARQL for querying, LOD allows for comprehensive and unified access to all datasets. Considering that the European standard DATEX II is widely used in many priority areas and services mentioned in the ITS Directive, LOD DATEX II was developed as a complementary approach to DATEX II XML. This facilitates the accessibility and comprehensibility of European traffic data and services. As part of this development, an ontological model called dtx_srti, based on the DATEX II Ontology, was created to support these efforts.