# Priority Maps for Surveillance and Intervention of Wildfires and other Spreading Processes

## TL;DR

This paper introduces a scalable optimization framework for creating priority maps to monitor and intervene in wildfire spread using UAVs, integrating landscape, wind, and fire dynamics for effective path planning.

## Contribution

It presents a novel method leveraging positive system properties to generate priority maps that adapt to changing wildfire dynamics for UAV surveillance and intervention.

## Key findings

- Successfully applied to 16 and 1000 node landscapes
- Demonstrates integration of fire dynamics, landscape, and wind conditions
- Shows how priority maps adapt to system changes

## Abstract

Unmanned Aerial Vehicle (UAV) path planning algorithms often assume a knowledge reward function or priority map, indicating the most important areas to visit. In this paper we propose a method to create priority maps for monitoring or intervention of dynamic spreading processes such as wildfires. The presented optimization framework utilizes the properties of positive systems, in particular the separable structure of value (cost-to-go) functions, to provide scalable algorithms for surveillance and intervention. We present results obtained for a 16 and 1000 node example and convey how the priority map responds to changes in the dynamics of the system. The larger example of 1000 nodes, representing a fictional landscape, shows how the method can integrate bushfire spreading dynamics, landscape and wind conditions. Finally, we give an example of combining the proposed method with a travelling salesman problem for UAV path planning for wildfire intervention.