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- Title
FIWARE-Compatible Smart Data Models for Satellite Imagery and Flood Risk Assessment to Enhance Data Management.
- Authors
Kouloglou, Ioannis-Omiros; Antzoulatos, Gerasimos; Vosinakis, Georgios; Lombardo, Francesca; Abella, Alberto; Bakratsas, Marios; Moumtzidou, Anastasia; Maltezos, Evangelos; Gialampoukidis, Ilias; Ouzounoglou, Eleftherios; Vrochidis, Stefanos; Amditis, Angelos; Kompatsiaris, Ioannis; Ferri, Michele
- Abstract
The increasing rate of adoption of innovative technological achievements along with the penetration of the Next Generation Internet (NGI) technologies and Artificial Intelligence (AI) in the water sector are leading to a shift to a Water-Smart Society. New challenges have emerged in terms of data interoperability, sharing, and trustworthiness due to the rapidly increasing volume of heterogeneous data generated by multiple technologies. Hence, there is a need for efficient harmonization and smart modeling of the data to foster advanced AI analytical processes, which will lead to efficient water data management. The main objective of this work is to propose two Smart Data Models focusing on the modeling of the satellite imagery data and the flood risk assessment processes. The utilization of those models reinforces the fusion and homogenization of diverse information and data, facilitating the adoption of AI technologies for flood mapping and monitoring. Furthermore, a holistic framework is developed and evaluated via qualitative and quantitative performance indicators revealing the efficacy of the proposed models concerning the usage of the models in real cases. The framework is based on the well-known and compatible technologies on NGSI-LD standards which are customized and applicable easily to support the water data management processes effectively.
- Subjects
REMOTE-sensing images; DATA management; DATA modeling; FLOOD warning systems; ARTIFICIAL intelligence; WATER management; FLOOD risk
- Publication
Information (2078-2489), 2024, Vol 15, Issue 5, p257
- ISSN
2078-2489
- Publication type
Article
- DOI
10.3390/info15050257