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AUTOMATIC DETECTION OF BUILDING DAMAGE CAUSED BY HURRICANE ON FLORIDA COASTAL AREA FROM AERIAL IMAGES
| Title: | AUTOMATIC DETECTION OF BUILDING DAMAGE CAUSED BY HURRICANE ON FLORIDA COASTAL AREA FROM AERIAL IMAGES. |
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| Name(s): |
Gyegyiri, Joseph , author Su, Hongbo, Thesis advisor Florida Atlantic University, Degree grantor Department of Civil, Environmental and Geomatics Engineering College of Engineering and Computer Science |
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| Type of Resource: | text | |
| Genre: | Electronic Thesis Or Dissertation | |
| Date Created: | 2024 | |
| Date Issued: | 2024 | |
| Publisher: | Florida Atlantic University | |
| Place of Publication: | Boca Raton, Fla. | |
| Physical Form: | application/pdf | |
| Extent: | 83 p. | |
| Language(s): | English | |
| Abstract/Description: | 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. | |
| Identifier: | FA00014427 (IID) | |
| Degree granted: | Thesis (MS)--Florida Atlantic University, 2024. | |
| Collection: | FAU Electronic Theses and Dissertations Collection | |
| Note(s): | Includes bibliography. | |
| Subject(s): |
Remote-sensing images Natural disasters Natural disasters--Data processing |
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| Persistent Link to This Record: | http://purl.flvc.org/fau/fd/FA00014427 | |
| Use and Reproduction: | Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
| Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
| Host Institution: | FAU |
