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This is my bachelor (EEE) project and the aim of this machine learning study is to apply support vector machine classification to disaster damage assessment, specifically for the 2010 Haiti earthquake. We used an input dataset which computed with the local self-similarity descriptor for the change detection with footprints and compared pre- and post- images and deformations of the areas in medium and high resolution satellite images. The performance criteria of this classification simulations are 75% true positive and 28% false positive damaged buildings on the average.
Project documents can be accessed from here:
![](http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif)
Supervised Classification Methods For Disaster Damage Assessment
At this study we applied the Support Vector Machine Classification Method to disaster damage assessment. We used an input dataset which computed with the local self-similarity descriptor for the change detection with footprints and compared pre- and
Source Code (Github):