Detection of Tornado Damage via Convolutional Neural Networks and Unmanned Aerial System Photogrammetry
| dc.contributor.author | Carani, Samuel James | en |
| dc.contributor.committeechair | Pingel, Thomas | en |
| dc.contributor.committeemember | Shao, Yang | en |
| dc.contributor.committeemember | Ramseyer, Craig A. | en |
| dc.contributor.department | Geography | en |
| dc.date.accessioned | 2023-04-15T06:00:07Z | en |
| dc.date.available | 2023-04-15T06:00:07Z | en |
| dc.date.issued | 2021-10-21 | en |
| dc.description.abstract | Disaster damage assessments are a critical component to response and recovery operations. In recent years, the field of remote sensing has seen innovations in automated damage assessments and UAS collection capabilities. However, little work has been done to explore the intersection of automated methods and UAS photogrammetry to detect tornado damage. UAS imagery, combined with Structure from Motion (SfM) output, can directly be used to train models to detect tornado damage. In this research, we develop a CNN that can classify tornado damage in forests using SfM-derived orthophotos and digital surface models. The findings indicate that a CNN approach provides a higher accuracy than random forest classification, and that DSM-based derivatives add predictive value over the use of the orthophoto mosaic alone. This method has the potential to fill a gap in tornado damage assessment, as tornadoes that occur in wooded areas are typically difficult to survey on the ground and in the field; an improved record of tornado damage in these areas will improve our understanding of tornado climatology. | en |
| dc.description.abstractgeneral | Disaster damage assessments are a critical component to response and recovery operations. In recent years, the field of remote sensing has seen innovations in automated damage assessments and Unmanned Aerial System (UAS) collection capabilities. However, little work has been done to explore the intersection of automated methods and UAS imagery to detect tornado damage. UAS imagery, combined with 3D models, can directly be used to train machine learning models to automatically detect tornado damage. In this research, we develop a machine learning model that can classify tornado damage in forests using UAS imagery and 3D derivatives. The findings indicate that the machine learning model approach provides a higher accuracy than traditional techniques. In addition, the 3D derivatives add value over the use of only the UAS imagery. This method has the potential to fill a gap in tornado damage assessment, as tornadoes that occur in wooded areas are typically difficult to survey on the ground and in the field; an improved record of tornado damage in these areas will improve our understanding of tornado climatology. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:32869 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/114516 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Photogrammetry | en |
| dc.subject | Tornado Damage | en |
| dc.subject | UAS | en |
| dc.subject | CNNs | en |
| dc.title | Detection of Tornado Damage via Convolutional Neural Networks and Unmanned Aerial System Photogrammetry | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Geography | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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