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Proceedings of the Future Technologies Conference

FTC 2019: Proceedings of the Future Technologies Conference (FTC) 2019 pp 100–111Cite as

Machine Learning Classification and Segmentation of Forest Fires in Wide Area Motion Imagery

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1070)

Abstract

Numerous models and simulations exist for characterizing and predicting wildland fire behavior. The U.S. Forest Service (USFS) and other organizations have devoted decades of research to identifying the parameters that affect fire movement, rate of spread, and direction of spread across geographic terrain. While this research is invaluable to the firefighting community, due to computational constraints, these models do not run in real time or against imagery at time-of-collect, and therefore do little to assist the firefighter and first responders on the ground during a wildland fire event. We present the first part of a multi-step automated computational methodology to characterize fire behavior and rate of spread in real time across any geographic terrain. This first step is the classification and segmentation of the wildland fire in Wide Area Motion Imagery (WAMI) using Machine Learning (ML) methods. The continuation of this research, detailed herein, will involve training a more robust, purpose-built Recurrent Neural Network architecture incorporating many of the parameters the USFS has been studying for decades. The goal of this research is to deploy models using ‘lite’ ML frameworks on edge devices, mounted on collection platforms for real-time decision support for firefighting operations as imagery and other data are being collected during a wildland fire event.

Keywords

  • Machine learning
  • Forest fires
  • Wide Area Motion Imagery
  • Image classification
  • Image segmentation

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  • DOI: 10.1007/978-3-030-32523-7_7
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(Source Hantson et al. 2016)

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(Source: Alkhoshi & Belkasim)

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Authors and Affiliations

  1. Next Tier Concepts, Inc., 8150 Leesburg Pike Suite 1400, Vienna, VA, 22182, USA

    Melonie Richey, David Blum, Meredith Gregory, Kendall Ocasio, Zachary Mostowsky & Jordan Chandler

Authors
  1. Melonie Richey
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  2. David Blum
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  3. Meredith Gregory
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  4. Kendall Ocasio
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  5. Zachary Mostowsky
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  6. Jordan Chandler
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Corresponding author

Correspondence to Melonie Richey .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Prof. Dr. Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Prof. Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Richey, M., Blum, D., Gregory, M., Ocasio, K., Mostowsky, Z., Chandler, J. (2020). Machine Learning Classification and Segmentation of Forest Fires in Wide Area Motion Imagery. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2019. FTC 2019. Advances in Intelligent Systems and Computing, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-030-32523-7_7

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