Detection of Tornado Damage via Convolutional Neural Networks and Unmanned Aerial System Photogrammetry

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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Geography

Department dc:contributor.department

Geography

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2021

Author dc:creator

• Carani, Samuel James

Chair dc:contributor.committeechair

• Pingel, Thomas

Committee members dc:contributor.committeemember

• Shao, Yang
• Ramseyer, Craig A.

• Photogrammetry
• Tornado Damage
• UAS
• CNNs

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:32869

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/114516