Contact us

Project in progress

SIGTE

Territorial Intelligence System for the Analysis and Simulation of Decisions in Rural Municipalities and Their Business Ecosystem

Read the project summary

Project summary

S.I.G.T.E. aims to develop a methodological and technological framework to analyse, model and simulate people flows in rural municipalities with cultural heritage. The project will investigate new territorial characterisation methodologies, structured digital models, simulation algorithms and predictive models based on artificial intelligence, to improve understanding of the dynamics of these environments and provide advanced tools to support municipal and business decision-making.

The results will focus on generating knowledge, validating it in representative environments and transferring it as a methodology that can be replicated in other municipalities in Castile and León.

Consortium members

  • AEICE (coordinator)
  • Fundación Las Edades del Hombre
  • MARES Virtuales
  • UNED-Ponferrada
  • VOXEL3D
AEICE, Fundación Las Edades del Hombre, MARES Virtuales, UNED-Ponferrada and Voxel

Co-funding

This project has received a grant from the Regional Ministry of Economy and Finance of the Junta de Castilla y León through the 2026 call supporting Innovative Business Clusters (AAEEII), co-financed by the European Regional Development Fund (ERDF), with the objective of “A more competitive and smarter Europe”.

Co-funded by the European Union; Ministry of Finance; European Funds; Junta de Castilla y León, Regional Ministry of Economy and Finance
Project reference
AEI/26/22
Total project budget
84.110,11 €
Total grant awarded
47.066,66 €

Objectives

  • Define a dedicated framework for the typological characterisation of rural municipalities with cultural heritage.
  • Design a structured digital model of the territory, forming the basis of a conceptual digital twin, with a modular and scalable architecture.
  • Develop algorithms for the dynamic simulation of people flows, including high-attendance events and emergencies.
  • Develop predictive models based on artificial intelligence adapted to environments with low data density.
  • Validate the system through demonstrators in relevant environments and generate tools to support municipal and business decisions, replicable in other municipalities.

Expected results

  • SIGTE methodological framework and territorial classification system.
  • Integrated digital territorial model.
  • Report on the dynamic simulation of flows and scenarios.
  • Validation report for the models in simulated operational environments, using the Duero territory as a case study, and a replicable methodological framework (TRL 5).
All R&D projects