Multimodal Wildland Fire Smoke Detection
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Published:2023-05-27
Issue:11
Volume:15
Page:2790
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Bhamra Jaspreet Kaur1ORCID, Anantha Ramaprasad Shreyas1, Baldota Siddhant1ORCID, Luna Shane2, Zen Eugene2, Ramachandra Ravi2, Kim Harrison2, Schmidt Chris3ORCID, Arends Chris4, Block Jessica5, Perez Ismael5, Crawl Daniel5, Altintas Ilkay5, Cottrell Garrison W.1ORCID, Nguyen Mai H.5ORCID
Affiliation:
1. Computer Science & Engineering, University of California San Diego, La Jolla, CA 92093, USA 2. Jacobs School of Engineering, University of California San Diego, La Jolla, CA 92093, USA 3. Space Science and Engineering Center, University of Wisconsin-Madison, Madison, WI 53706, USA 4. San Diego Gas & Electric, San Diego, CA 92123, USA 5. San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
Abstract
Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have, in turn, led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgent need to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. To that end, in this paper, we present our work on integrating multiple data sources into SmokeyNet, a deep learning model using spatiotemporal information to detect smoke from wildland fires. We present Multimodal SmokeyNet and SmokeyNet Ensemble for multimodal wildland fire smoke detection using satellite-based fire detections, weather sensor measurements, and optical camera images. An analysis is provided to compare these multimodal approaches to the baseline SmokeyNet in terms of accuracy metrics, as well as time-to-detect, which is important for the early detection of wildfires. Our results show that incorporating weather data in SmokeyNet improves performance numerically in terms of both F1 and time-to-detect over the baseline with a single data source. With a time-to-detect of only a few minutes, SmokeyNet can be used for automated early notification of wildfires, providing a useful tool in the fight against destructive wildfires.
Subject
General Earth and Planetary Sciences
Reference40 articles.
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