UVicSpace

An evaluation of deep learning semantic segmentation for land cover classification of oblique ground-based photography

Show simple item record

dc.contributor.author Rose, Spencer
dc.date.accessioned 2020-10-01T00:12:24Z
dc.date.available 2020-10-01T00:12:24Z
dc.date.copyright 2020 en_US
dc.date.issued 2020-09-30
dc.identifier.uri http://hdl.handle.net/1828/12156
dc.description.abstract This thesis presents a case study on the application of deep learning methods for the dense prediction of land cover types in oblique ground-based photography. While deep learning approaches are widely used in land cover classification of remote-sensing data (i.e., aerial and satellite orthoimagery) for change detection analysis, dense classification of oblique landscape imagery used in repeat photography remains undeveloped. A performance evaluation was carried out to test two state-of the-art architectures, U-net and Deeplabv3+, as well as a fully-connected conditional random fields model used to boost segmentation accuracy. The evaluation focuses on the use of a novel threshold-based data augmentation technique, and three multi-loss functions selected to mitigate class imbalance and input noise. The dataset used for this study was sampled from the Mountain Legacy Project (MLP) collection, comprised of high-resolution historic (grayscale) survey photographs of Canada’s Western mountains captured from the 1880s through the 1950s and their corresponding modern (colour) repeat images. Land cover segmentations manually created by MLP researchers were used as ground truth labels. Experimental results showed top overall F1 scores of 0.841 for historic models, and 0.909 for repeat models. Data augmentation showed modest improvements to overall accuracy (+3.0% historic / +1.0% repeat), but much larger gains for under-represented classes. en_US
dc.language English eng
dc.language.iso en en_US
dc.rights Available to the World Wide Web en_US
dc.subject landscape classification en_US
dc.subject semantic segmentation en_US
dc.subject change detection en_US
dc.subject deep learning en_US
dc.title An evaluation of deep learning semantic segmentation for land cover classification of oblique ground-based photography en_US
dc.type Thesis en_US
dc.contributor.supervisor Coady, Yvonne
dc.degree.department Department of Computer Science en_US
dc.degree.level Master of Science M.Sc. en_US
dc.description.scholarlevel Graduate en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search UVicSpace


Browse

My Account

Statistics

Help