PhD Thesis Proposal - Xiao Fu

Title: Computational Wildfire-proneLandscape Design and Mapping  
 
Date and Time: Nov 13, 2 pm - 3:30 pm
 
Location: SAL 300     
 
Committee members: Barath Raghavan, Bhaskar Krishnamachari, Ramesh Govindan, Peter Beerel, and Dani Yogatama    
 
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 wildfire mapping work and reduces wildfire mapping time from hours to seconds.In future work, we propose a complete system that ensures landscape monitoring beyond the early wildfire propagation phase. We then emphasize multimodal approaches to landscape understanding for adaptive fuel analysis. Beyond monitoring the wildfire expansion, future systems can structurally understand the shifting landscape.     

Location: Henry Salvatori Computer Science Center (SAL) - 300

Audiences: Everyone Is Invited

Contact: Ellecia Williams