FireLoc: Low-latency Multi-modal Wildfire Geolocation
Pages 1 - 14
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.
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Index Terms
- FireLoc: Low-latency Multi-modal Wildfire Geolocation
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Published In
November 2024
950 pages
ISBN:9798400706974
DOI:10.1145/3666025
- Chair:
- Jie Liu,
- Co-chairs:
- Yuanchao Shu,
- Jiming Chen,
- Program Chair:
- Yuan He,
- Program Co-chair:
- Rui Tan
This work is licensed under a Creative Commons Attribution-ShareAlike International 4.0 License.
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SenSys '24
SenSys '24: 22nd ACM Conference on Embedded Networked Sensor Systems
November 4 - 7, 2024
Hangzhou, China
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Overall Acceptance Rate 174 of 867 submissions, 20%
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