Object-based classification of earthquake damage from high-resolution optical imagery using machine learning

James Bialas*, Thomas Oommen, Umaa Rebbapragada, Eugene Levin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


Object-based approaches in the segmentation and classification of remotely sensed images yield more promising results compared to pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods are often used, but yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time-sensitive applications, such as earthquake damage assessment. Herein, we used a systematic approach in evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely sensed imagery. We tested a variety of algorithms and parameters on post-event aerial imagery for the 2011 earthquake in Christchurch, New Zealand. Results were compared against manually selected test cases representing different classes. In doing so, we can evaluate the effectiveness of the segmentation and classification of different classes and compare different levels of multistep image segmentations. Our classifier is compared against recent pixel-based and object-based classification studies for postevent imagery of earthquake damage. Our results show an improvement against both pixel-based and object-based methods for classifying earthquake damage in high resolution, post-event imagery.

Original languageEnglish
Article number036025
JournalJournal of Applied Remote Sensing
Issue number3
StatePublished - 1 Jul 2016
Externally publishedYes


FundersFunder number
National Science Foundation1300720
National Aeronautics and Space Administration


    • GEOBIA
    • earthquake
    • machine learning
    • object-based classification


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