Next Article in Journal
Precise and Robust Ship Detection for High-Resolution SAR Imagery Based on HR-SDNet
Next Article in Special Issue
Potential Underestimation of Satellite Fire Radiative Power Retrievals over Gas Flares and Wildland Fires
Previous Article in Journal
SHAtropE—A Regional Gridded ZTD Model for China and the Surrounding Areas
Previous Article in Special Issue
The Effect of Snow Depth on Spring Wildfires on the Hulunbuir from 2001–2018 Based on MODIS
Open AccessLetter

Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images

Lawrence Berkeley National Lab, Berkeley, CA 94720, USA
Fireball LLC, Reno, NV 89509, USA
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;
Received: 25 November 2019 / Revised: 23 December 2019 / Accepted: 30 December 2019 / Published: 2 January 2020
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 smoke detection; fire detection; machine learning
Show Figures

Graphical abstract

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop