Abstract

Firefighters still rely on coarse remote sensing and inaccurate eyewitness reports to localize spreading wildfires. Despite advances in sensing, UAVs, and computer vision, the community has yet to combine the right modalities to achieve effective wildfire geolocalization and spotting. We present FireLoc, a fast and accurate wildfire crowdsensing system that localizes and maps wildfires combining ground cameras and landscape data.
Prior image-based localization techniques fail in vegetated areas as they are tuned for close-range human-built environments. Instead, FireLoc integrates monocular depth mapping models, topography models, and cross-camera methods to achieve over 1000m range in vegetated environments leveraging low-cost smartphones. Due to the paucity of historical wildfire data, we built a wildfire simulator to provide additional data for validation. We show that FireLoc surpasses prior …

Date

November 4, 2024

Authors

Xiao Fu, Yue Hu, Prashanth Sutrave, Peter A Beerel, Barath Raghavan

Book

Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems

Pages

1-14