Computer Vision Techniques for Damage Assessment from High Resolution Remote Sensing Imagery

Doctoral Dissertation


Techniques in post-disaster assessment from remote sensing imagery have been studied by different research communities in the past decade. Such an assessment benefits everybody from government organizations and insurance agencies to individual home owners. This work explores the application of existing and novel computer vision algorithms for an automated damage assessment caused by windstorm. The various subproblems studied include geometric and photometric correction, rooftop recognition and change classification based on textural differences. Past work done in this area by remote sensing, geoscience, civil engineering and image processing communities had established that the problems addressed in these areas were challenging and largely unsolved. The solutions proposed in this work are strongly motivated towards building a system capable of fast, robust and fine-grained damage analysis from aerial or satellite imagery. The algorithms introduced are thoroughly evaluated and compared with previous works. The results demonstrate that this work promises higher leaps in the field of automated damage classification and provides insights into the reliability of such analysis in real world scenarios.


Attribute NameValues
  • etd-12032012-232906

Author Jim O Thomas
Advisor Kevin W Bowyer
Contributor Ahsan Kareem, Committee Co-Chair
Contributor Aaron Striegel , Committee Member
Contributor Scott Emrich, Committee Member
Contributor Kevin W Bowyer, Committee Chair
Contributor Patrick Flynn, Committee Member
Degree Level Doctoral Dissertation
Degree Discipline Computer Science and Engineering
Degree Name Doctor of Philosophy
Defense Date
  • 2012-09-14

Submission Date 2012-12-03
  • United States of America

  • machine learning

  • computer vision

  • image processing

  • building detection

  • aerial images

  • damage assessment

  • University of Notre Dame

  • English

Record Visibility Public
Content License
  • All rights reserved

Departments and Units

Digital Object Identifier


This DOI is the best way to cite this doctoral dissertation.


Please Note: You may encounter a delay before a download begins. Large or infrequently accessed files can take several minutes to retrieve from our archival storage system.