SIDENCA: Intelligent System for Risk Detection and Decision-Making in the Face of Catastrophes

Funding institution: Generalitat Valenciana (through IVACE+i) and the European Union (European Funds)
Total budget: €658,392.74/ Budget UJI: €135,908.00
Duration: October 2025 – December 2027

SIDENCA is a strategic cooperation project aimed at developing an interoperable, scalable, and multisector platform to support prevention, real-time response, and post-event assessment of extreme climate events in the Comunitat Valenciana (wildfires, floods, severe storms, heat waves, and droughts). The core outcome will be a web geoportal (desktop + mobile) that integrates multisource geospatial and sensor data, advanced GeoAI and Bayesian spatio-temporal modelling, and semantic interoperability to empower decision-makers, emergency services, civil protection, and territorial planners with actionable, context-aware information across the full emergency-management cycle. The project builds on a TRL6 operational pilot already deployed with real end-users (Torrent) and targets TRL7 through scaling, modularisation, and expanded multi-risk capabilities.

The design is based on three basic pillars:

– Multisource geospatial intelligence for multi-risk management. SIDENCA fuses Earth Observation (Sentinel/PlanetScope, thermal and radar products), drones (hyperspectral/LiDAR), in-situ/IoT sensing, bio-physical variables, and socio-economic vulnerability layers to generate dynamic risk, damage, and event evolution maps (pre-, during, and post-event).

– Advanced modelling and AI for prediction and impact assessment. The platform integrates Bayesian INLA-SPDE and propagation processes (e.g., RSP) with ML/DL approaches (e.g., CNN/ConvLSTM, RF/SVM) to produce probabilistic predictions, severity indicators, and decision-support products under uncertainty, tailored to Mediterranean multi-hazard dynamics.

– Operational interoperability and resilient edge–cloud operation. SIDENCA adopts a semantic data architecture (ontologies, RDF/GeoSPARQL/JSON-LD, rules) to ensure interoperable integration and automated reasoning, and adds edge intelligence (field-deployed nodes capable of local inference and autonomous alerts) to maintain operation even with limited connectivity.

Partnership

Coordinator

Albavalor S.L.U.

Partners

Albavalor, AINIA, Innovageo Software, Laberit Sistemas, Universitat Jaume I (UJI), Universitat de València (UVEG)

Funding

IP 

Pablo Juan Verdoy (juan@uji.es)

Technical contact  

Sergi Trilles (strilles@uji.es)