Volume 9, Issue 11 (November 2022), Pages: 34-43

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 Original Research Paper

 Wildfires damage assessment Via LiDAR

 Author(s): Vivian Sultan *, Taron Sarksyan, Savita Yadav

 Affiliation(s):

 College of Business and Economics, California State University, Los Angeles, USA

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-1066-5212

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.11.004

 Abstract:

This paper examines the phenomenon of wildfires in California and investigates the buildings affected by the Woolsey Fire in Central Malibu in 2018. We focus empirically on machine learning to identify damaged objects from point-cloud data. This project includes a literature review with references to methods used for wildfire research and LiDAR data processing. In this study, researchers trained an existing deep learning model to determine if it offers an effective solution for extracting damaged objects. Data sources for this study include point-cloud data retrieved via the LidarExplorer tool and Kaggle’s 2013–2020 California wildfire data. Using two layers of building footprints in the Malibu “T-Zone” revealed 907 structures, of which 435 were damaged or destroyed based on map observations. This analysis of structure identification supports the literature that deep learning can successfully classify objects damaged by wildfires.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: LiDAR, ArcGIS, Damaged objects, Deep learning

 Article History: Received 4 April 2022, Received in revised form 20 July 2022, Accepted 26 July 2022

 Acknowledgment 

This research is based on students’ course project work at the graduate school, Information Systems department of California State University, Los Angeles. The second and the third author would like to acknowledge Professor Vivian Sultan for her guidance and contribution to this research project. This research received no external funding.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Sultan V, Sarksyan T, and Yadav S (2022). Wildfires damage assessment Via LiDAR. International Journal of Advanced and Applied Sciences, 9(11): 34-43

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