Author Type

Graduate Student

Date of Award

2024

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Su, Hongbo

Abstract

Rapid response and efficient damage assessment are life-or-death matters in the wake of natural disasters such as hurricanes and earthquakes. These events wreak havoc on infrastructure and properties and, most critically, endanger human lives. The timely and effective allocation of resources during such crises is imperative, necessitating meticulous planning based on the extent of damage incurred. This research presents an approach to automating the damage assessment using pre/post-disaster aerial images and computer vision. Recent advancements in disaster response strategies have encouraged researchers to harness the power of satellite and aerial imagery to assess the aftermath. Usually, due to the different characteristics between training datasets and available datasets in times of disasters, retraining the model to improve detection accuracy has been the norm, even though it is time and resource intensive. Our method surpasses conventional solutions and requires no retraining or fine-tuning on disaster-specific data. An existing model was retrained and improved on a diverse building damage dataset and demonstrably generalizes to new disaster scenarios. Having achieved higher performances compared to state of the art models, we determines our models real world applicability by using Hurricane Ian as our potent study grounds.

Extent

83 p.

Comments

Includes bibliography.|Thesis (MS)--Florida Atlantic University, 2024.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00014427

Format

pdf

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