TechRxiv

A Geospatial Approach to Wild fire Risk Modeling using Machine Learning and Remote Sensing Data

Download (711.54 kB)
preprint
posted on 2023-12-05, 04:00 authored by Riya Gupta, Hudson KimHudson Kim

In recent years, the likelihood of wildfire occurrence has increased in many North American communities as changes in climate have led to longer, more deadly fire seasons.  Many Americans, especially those living in Western states, have reported frequent drought and wildfire conditions, leading to an increased need for a modeling program to assess wildfire risk at a low computational cost. The research objective of this paper was to develop a machine learning model capable of producing real-time wildfire risk assessments using five geospatial datasets: Land Fire Mean Return, Annual Precipitation, Sentinel-2 Imagery, Land Cover, and Moisture Deficit & Surplus. To create the model, three separate machine learning architectures were implemented (U-Net, DeepLabV3, and the Pyramid Scene Parsing Network) and then applied to the study area of San Bernardino County, CA for the year 2020. In addition, this study demonstrated a proof of concept for further inquiry into combining artificial intelligence and geospatial datasets to create useful insights.  

Funding

NASA Award NNX16AB89A

History

Email Address of Submitting Author

hudson.n.kim@gmail.com

ORCID of Submitting Author

0009-0009-5554-5212

Submitting Author's Institution

Westview High School

Submitting Author's Country

  • United States of America

Usage metrics

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC