Deep learning for Wild Fire Progress Prediction

NAISS 2023/5-75


NAISS Medium Compute

Principal Investigator:

Josephine Sullivan


Kungliga Tekniska högskolan

Start Date:


End Date:


Primary Classification:

10599: Other Earth and Related Environmental Sciences

Secondary Classification:

10207: Computer Vision and Robotics (Autonomous Systems)

Tertiary Classification:

20703: Remote Sensing



Wild fires are increasing each year in their intensity and frequency as climate changes accelerate. These wild fires can have catastrophic effects both on the local populations and the environment. Thus there is a critical need to develop better algorithms to predict their temporal progress to help manage their progress and lessen their impact. In this project we will investigate and develop methods for the prediction wild fire progress imaged with low spatial resolution but high temporal resolution satellites in combination with weather data and topographic descriptors. We will focus on harnessing auto-regression deep learning approaches such, as diffusion models, to make these predictions. In the second half of the project we will also investigate the possibility of upgrading low-spatial resolution predictions to higher-spatial resolution ones.