Description and objectives

The project aims to develop advanced AI algorithms for assessing fire risk in extreme environments, where artificial structures such as roads and railways are embedded within predominantly natural landscapes including grasslands, shrublands, and forests. The algorithms will enable the analysis of vegetation health and the detection of waste or combustible materials using images captured by visible-light cameras. The collected data will support the creation of risk scenarios, highlighting the times of year or conditions most favorable to fire development in the monitored areas.

By combining these algorithms with active monitoring tools already developed by WaterView—capable of detecting smoke plumes and flames through cameras installed in open environments—the project will provide a comprehensive solution for automated fire surveillance, including in remote or sparsely populated areas.
Unlike most existing market solutions, which rely primarily on satellite imagery, this system enables more granular monitoring with higher spatial resolution, focusing on locations most vulnerable to fire events.

Objectives:

• Develop AI algorithms to assess vegetation health and identify combustible materials or waste.
• Generate high-resolution fire risk scenarios to identify the most fire-prone periods and conditions.
• Integrate with active monitoring systems for real-time detection of smoke and flames.
• Provide a scalable, automated solution for fire surveillance, including in hard-to-access areas.
• Offer a more detailed and precise spatial monitoring capability compared to satellite-based systems.