51³Ô¹Ï

Topic

MountainScape Semantic Segmentation of Historical and Repeat Images

Department of Computer Science

Date & location

• Monday, March 17, 2025

• 1:30 P.M.

• Virtual Defence

Reviewers

Supervisory Committee

• Dr. George Tzanetakis, Department of Computer Science, 51³Ô¹Ï (Co-Supervisor)

• Dr. Eric Higgs, Department of Computer Science, UVic (Co-Supervisor) 

External Examiner

• Dr. Alexandra Branzan Albu, Department of Electrical and Computer Engineering, UVic 

Chair of Oral Examination

• Dr. Hong-Chuan Yang, Department of Electrical and Computer Engineering, UVic

   

Abstract

Semantic segmentation of ultra-high resolution images is challenging due to high memory and computation requirements. Current approaches to this problem involve cropping the ultra-high resolution image into small patches for individual processing in order to provide local context, or under-sampling the images to provide global context, or following a combination of both which gives rise to global-local refinement pipelines. In this thesis, we present the MountainScape Segmentation Dataset (MS2D) which comprises high-resolution historic (grayscale) manually segmented images of Canadian mountain landscapes captured from 1861 to 1958 and their corresponding modern (colour) repeat images. Additionally, we analyze the characteristics of the dataset, define evaluation criteria, and provide a baseline to serve as a reference benchmark for automated land cover classification using the Python Landscape Classification Tool (PyLC), an existing software tool. The main contribution of this thesis lies in the experimental exploration of various deep learning architectures and a comprehensive investigation utilizing a larger dataset than that employed in the original PyLC study.