Under copyright Constraint(s) on Use: This work is protected by the U.S. Copyright Law (Title 17, U.S.C.). Use of this work beyond that allowed by "fair use" or any license applied to this work requires written permission of the copyright holder(s). Responsibility for obtaining permissions and any use and distribution of this work rests exclusively with the user and not the UC San Diego Library. Inquiries can be made to the UC San Diego Library program having custody of the work. Use: This work is available from the UC San Diego Library. This digital copy of the work is intended to support research, teaching, and private study.
Rights Holder and Contact
Luna, Shane; Zen, Eugene; Kim, Harrison ; Cabasag, Paul ; Ramachandra, Ravi
Description
Wildfires have caused significant damages throughout the state of California. In 2018 alone, it is estimated that approximately $150 billion of damages were caused by wildfires . The devastating impact of these fires in recent years has intensified the need for hardened infrastructure systems and more advanced fire prevention and mitigation efforts. This project explores improving a pre-existing, image classification model for smoke detection, called SmokeyNet , originally developed by the San Diego Supercomputer Center at the University of California, San Diego. SmokeyNet uses images that are captured from a network of cameras across Southern California known as the High Performance Wireless Research and Education Network (HPWREN) to predict and identify fire smoke. Improvements were explored by 1) incorporating supplementary geostationary satellite fire detection model data (Wildfire Automated Biomass Burning Algorithm, WFABBA) to increase model accuracy, and 2) programmatically geolocating the approximate origin of a fire from an image to aid in the emergency response process. Weather data was analyzed as an additional supplementary dataset to the SmokeyNet model, but retraining and retesting was not fully complete. From the ensemble of the multiple fire models, it resulted in a slight boost to overall accuracy. When programmatically geolocating the fire, by using triangulation, location approximations were able to be calculated within 3 miles of actuals; however, a small sample size of 10 was used and further data would need to be gathered and evaluated. Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) Luna, Shane; Zen, Eugene; Kim, Harrison; Cabasag, Paul; Ramachandra, Ravi; Nguyen, Mai; Crawl, Dan; Perez, Ismael; Block, Jessica (2022). Wildfire Smoke Detection Analysis. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0R49QXV
Type
dataset
Identifier
ark:/20775/bb8815458t
Language
English
Subject
Smoke detection Fire occurrence Weather Satellite imagery Fire severity Wildfires Fire risk Capstone projects Data Science & Engineering Master of Advanced Study (DSE MAS) DSE MAS - 2022 Cohort
If you're wondering about permissions and what you can do with this item, a good starting point is the "rights information" on this page. See our terms of use for more tips.
Share your story
Has Calisphere helped you advance your research, complete a project, or find something meaningful? We'd love to hear about it; please send us a message.
