Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake
| dc.contributor.author | Cooner, Austin Jeffrey | en |
| dc.contributor.committeechair | Shao, Yang | en |
| dc.contributor.committeemember | Campbell, James B. Jr. | en |
| dc.contributor.committeemember | Prisley, Stephen P. | en |
| dc.contributor.department | Geography | en |
| dc.date.accessioned | 2016-12-20T09:00:26Z | en |
| dc.date.available | 2016-12-20T09:00:26Z | en |
| dc.date.issued | 2016-12-19 | en |
| dc.description.abstract | Remote sensing continues to be an invaluable tool in earthquake damage assessments and emergency response. This study evaluates the effectiveness of multilayer feedforward neural networks, radial basis neural networks, and Random Forests in detecting earthquake damage caused by the 2010 Port-au-Prince, Haiti 7.0 moment magnitude (Mw) event. Additionally, textural and structural features including entropy, dissimilarity, Laplacian of Gaussian, and rectangular fit are investigated as key variables for high spatial resolution imagery classification. Our findings show that each of the algorithms achieved nearly a 90% kernel density match using the United Nations Operational Satellite Applications Programme (UNITAR/UNOSAT) dataset as validation. The multilayer feedforward network was able to achieve an error rate below 40% in detecting damaged buildings. Spatial features of texture and structure were far more important in algorithmic classification than spectral information, highlighting the potential for future implementation of machine learning algorithms which use panchromatic or pansharpened imagery alone. | en |
| dc.description.abstractgeneral | Satellite imagery can help emergency managers to better respond to the aftermath of deadly earthquakes. This study evaluates the effectiveness of machine learning algorithms in detecting earthquake damage caused by the 2010 Port-au-Prince, Haiti event. Additionally, textural and structural features of high resolution black and white imagery are investigated as key variables for damage classification. We found that each of the algorithms achieved nearly a 90% wide area damage density match using the United Nations Operational Satellite Applications Programme (UNITAR/UNOSAT) dataset as validation. The most effective algorithm achieved an error rate below 40% in detecting damaged buildings. Spatial features of texture and structure were far more important in classification than spectral information, highlighting the potential for future implementation of machine learning algorithms which use panchromatic or pansharpened imagery alone. This would reduce data requirements and allow response resources to be allocated over space quicker and more efficiently. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:9338 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/73741 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | earthquake damage | en |
| dc.subject | Machine learning | en |
| dc.subject | computer vision | en |
| dc.subject | Random Forests | en |
| dc.subject | neural networks | en |
| dc.title | Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Geography | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |