Pre- and Post-Disaster Remote Sensing with Drones for Supporting Disaster Management

Small (< 25 kg) aerial drones have expanded the remote sensing toolkit for disaster management activities, resulting in hundreds of published case studies in the past two decades. The overall goal of this doctoral research, which comprises three related studies, is to evaluate drone-based pre- and post-disaster remote sensing as a tool to support disaster management. The first study provides a critical review of drone-based remote sensing in natural hazard-related disasters to highlight research trends, biases, and expose new opportunities. Recommendations for future research include a greater focus on demonstrating and evaluating drone-based support of pre-disaster data acquisition (a preparedness activity) and rapid damage assessment (a response activity). As such, the second study presents the first pre-disaster drone-based mapping mission over an urban area (downtown Victoria, British Columbia) approved by Transport Canada. The objective was to assess the quality of 3D data obtained with the only legally approved drone. Finally, the third study demonstrates rapid mapping of hurricane roof damage using artificial intelligence (deep learning) and drone imagery, including an accuracy assessment. Overall, this doctoral research identified critical knowledge gaps in the field of pre- and post-disaster remote sensing with small aerial drones, and then demonstrated and evaluated drone-based support of two building-damage-related activities that were recommended for future research.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Geography

Grantor dc:publisher.institution

Arts

Year dc:date.issued

2023

Author dc:creator

• Kucharczyk, Maja

Advisor dc:contributor.advisor

• Hugenholtz, Chris H.

Committee members dc:contributor.committeemember

• Geldsetzer, Torsten
• Hay, Geoffrey J.
• Moorman, Lynn
• Slick, Jean

• disaster
• hazard
• emergency
• damage
• drone
• UAV
• RPAS
• literature review
• remote sensing
• mapping
• photogrammetry
• structure from motion
• artificial intelligence
• deep learning
• convolutional neural network

Statement dc:rights

• University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

en

DOI

https://dx.doi.org/10.11575/PRISM/dspace/40980

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:1880/116135