Geo-Referenced Image Data Assimilation For Wildfire Spread Simulation

Abstract

Data assimilation is a method to improve the state inference by assimilating the real time observation data into the systems under study. In wildfire spread simulation, the deployed fire sensors are used to collect the real time temperatures, which can be infused into the wildfire spread simulation model for improved state predictions. However, for many scenarios, the temperature data is limited due to the unavailability of the deployed sensors. With the rapid development of social networking, many other types of data, such as the geo-referenced images, can be obtained from the public websites. In this paper, we propose the data assimilation method to assimilate the geo-referenced images into the wildfire spread simulation using sequential Monte Carlo (SMC) methods. We will show its effectiveness to improve the state estimation by the proposed data assimilation method through the designed experiments.