Politecnico di Torino - Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY

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DATA-DRIVEN WILDFIRE PROPAGATION SYSTEM

Aerial surveillanceFirefighter support systemInformatica Tsd EnRisk assessment systemWildfireWildfire propagation system

Introduction

The invention enables to improve the wildfire propagation prediction and risk assessment by integrating data coming from imaging sensors and wildfire propagation tools. The result of this integration is the estimation of unknown physical parameters and the precise geolocalization of the fire front. This firefighter support system has the aim to reduce damages, operative costs and impact on the ecosystem.

Technical features

The invention is composed of a software and a hardware part. The hardware consists of a thermal camera, a processor and a data communication system mounted on a platform, which can be a drone, an aircraft or a surveillance tower. The software takes the data coming from the sensors, extracts useful information and then integrates them with the wildfire simulator. The result of this integration is sent in real time to a web server and made accessible to firefighters and operational commanders. The possibility to access real time videos in remote operative centres simplifies the risk assessment and decision making tasks, with a more effective management of the available resources. The system is designed to be completely autonomous, by allowing a reduction of the operative costs. The integration system performs a parameter estimation task by inferring the wind intensity and direction, not easy to obtain from field measurements, but fundamental for an accurate wildfire propagation prediction

Possible Applications

  • Accurate prediction of wildfire;
  • Post-event forensic analysis;
  • Personnel formation tool;
  • Precise fire-ignition liquid release system;
  • Unsupervised first alert system;
  • UAV integration for firefighters operation support;
  • Web streaming platform for fast information release.

Advantages

  • Improved wildfire propagation prediction;
  • Estimation of physical parameters, otherwise difficult to measure;
  • Operative cost reduction;
  • Improved personnel safety;
  • Real time data availability from any platform and any place;
  • Unsupervised;
  • Multiplatform mounting system.