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

dc.contributor.authorRose, Spencer
dc.contributor.supervisorCoady, Yvonne
dc.date.accessioned2020-10-01T00:12:24Z
dc.date.available2020-10-01T00:12:24Z
dc.date.copyright2020en_US
dc.date.issued2020-09-30
dc.degree.departmentDepartment of Computer Scienceen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractThis 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.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/12156
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectlandscape classificationen_US
dc.subjectsemantic segmentationen_US
dc.subjectchange detectionen_US
dc.subjectdeep learningen_US
dc.titleAn evaluation of deep learning semantic segmentation for land cover classification of oblique ground-based photographyen_US
dc.typeThesisen_US

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