Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images
1
Lawrence Berkeley National Lab, Berkeley, CA 94720, USA
2
Fireball LLC, Reno, NV 89509, USA
3
Lawrence Berkeley National Lab and Dept. of Physics, University of California at Berkeley, Berkeley, CA 94720, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 166; https://doi.org/10.3390/rs12010166
Received: 25 November 2019
/
Revised: 23 December 2019
/
Accepted: 30 December 2019
/
Published: 2 January 2020
(This article belongs to the Special Issue Fire Remote Sensing: Capabilities, Innovations, Opportunities and Challenges)
Pioneering networks of cameras that can search for wildland fire signatures have been in development for some years (High Performance Wireless Research & Education Network—HPWREN cameras and the ALERT Wildfire camera). While these cameras have proven their worth in monitoring fires reported by other means, we have developed a functioning prototype system that can detect smoke from fires usually within 15 min of ignition, while averaging less than one false positive per day per camera. This smoke detection system relies on machine learning-based image recognition software and a cloud-based work-flow capable of scanning hundreds of cameras every minute. The system is operating around the clock in Southern California and has already detected some fires earlier than the current best methods—people calling emergency agencies or satellite detection from the Geostationary Operational Environmental Satellite (GOES) satellites. This system is already better than some commercial systems and there are still many unexplored methods to further improve accuracy. Ground-based cameras are not going to be able to detect every wildfire, and so we are building a system that combines the best of terrestrial camera-based detection with the best approaches to satellite-based detection.
View Full-Text
Keywords:
smoke detection; fire detection; machine learning
▼
Show Figures
Graphical abstract
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Govil, K.; Welch, M.L.; Ball, J.T.; Pennypacker, C.R. Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images. Remote Sens. 2020, 12, 166. https://doi.org/10.3390/rs12010166
AMA Style
Govil K, Welch ML, Ball JT, Pennypacker CR. Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images. Remote Sensing. 2020; 12(1):166. https://doi.org/10.3390/rs12010166
Chicago/Turabian StyleGovil, Kinshuk, Morgan L. Welch, J. T. Ball, and Carlton R. Pennypacker. 2020. "Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images" Remote Sensing 12, no. 1: 166. https://doi.org/10.3390/rs12010166
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
