PREDICAT: A Semantic Service-Oriented Platform for Data Interoperability and Linking in Earth Observation and Disaster Prediction

Abstract

The increasing volume of data generated by earth observation programs such as Copernicus, NOAA, and NASA Earth Data, is overwhelming. Although these programs are very costly, data usage remains limited due to lack of interoperability and data linking. In fact, multi-source and heterogeneous data exploitation could be significantly improved in different domains especially in the natural disaster prediction one. To deal with this issue, we introduce the PREDICAT project that aims at providing a semantic service-oriented platform to PREDIct natural CATastrophes. The PREDICAT platform considers (1) data access based on web service technology; (2) ontology-based interoperability for the environmental monitoring domain; (3) data integration and linking via big data techniques; (4) a prediction approach based on semantic machine learning mechanisms. The focus in this paper is to provide an overview of the PREDICAT platform architecture. A scenario explaining the operation of the platform is presented based on data provided by our collaborators, including the international intergovernmental Sahara and Sahel Observatory (OSS).

Semantic Integration of Raster Data for Earth Observation on Territorial Units

“…Finally, while we do not fully address the scalability of our approach, several works are dealing with the management of large volumes of EO data. In [14], a framework helps to integrate and process large-scale heterogeneous big data generated from multiple sources for decision support to prevent natural disasters. The semantic integration of EO and non-EO data is based on the MEMOn modular ontology that reuses the BFO (Basic Formal Ontology (https://basic-formal-ontology.org/, accessed date: 23 December 2021)), SSN, and ENVO (Environmental Ontology (https://sites.google.com/site/environmentontology, accessed date: 23 December 2021)) ontologies.…”

Semantically Enriched Crop Type Classification and Linked Earth Observation Data to Support the Common Agricultural Policy Monitoring

“…Sensors and observations are divided into many subcategories such as physical and meteorological, while events are associated with any hydrological cycle change. Modular Environmental Monitoring Ontology (MEMOn) [28] extends the abovementioned ontology as, except from sensor and observation data, it provides a structure to model a plethora of different aspects that are identified on an emergency situation under the environmental monitoring domain. The ontology provides the structures to represent environmental features (procedure and material), physical conditions (disaster) and spatiotemporal information (geolocation and time).…”

“…For data that are related with China multiple components have been developed to adapt in different interfaces, while for international data the GEO DAB agent is used. PREDICAT [28] is a system that focuses on natural catastrophes prediction. PREDICAT uses different ontologies to semantically represent the data that are pertinent to the system (semantic layer).…”

Towards Knowledge-Driven Automatic Service Composition for Wildfire Prediction

“…Figure 1 depicts our layered prediction system architecture [4] that includes five layers: Semantic layer, Knowledge layer, Application layer, Service Composition layer, and Service layer. The Semantic layer contains the domain and source ontologies.…”