Mountains of Confusion: Evaluating Image Enhancement to Improve AI Landscape Classification

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dc.contributor.authorBron, Larissa
dc.date.accessioned2024-03-16T13:40:51Z
dc.date.available2024-03-16T13:40:51Z
dc.date.issued2024
dc.description.abstractWith over 120,000 historic images of the Canadian Rockies, the Mountain Legacy Project (MLP) created the Python Landscape Classification tool (PyLC) to expedite the process of pixel-level identification of ecosystems used in quantifying ecosystem change from repeat photography. PyLC is a set of semantic segmentation artificial intelligence models that have been trained to discern ecosystems in historical and repeat images of mountain landscapes. This research project coincides with multiple efforts towards improving PyLC to better generalize ecosystem mapping to the large geographic region the image collection captures. The experimental approach was to trial image enhancements using Python functions in an attempt to improve PyLC’s differentiation of ecosystems by separately changing the contrast, noise, and sharpness of images. Following this, comparison of the median accuracy between trials revealed either no change or a decrease to accuracy, though each image enhancement did change how PyLC interpreted and mapped the ecosystems in the images. These results lend support toward the limited generalizability of PyLC to new images while providing a review of image enhancement methods that could be incorporated into improving training through data augmentation.
dc.description.reviewstatusReviewed
dc.description.scholarlevelUndergraduate
dc.description.sponsorshipJamie Cassels Undergraduate Research Awards (JCURA)
dc.identifier.urihttps://hdl.handle.net/1828/16122
dc.language.isoen
dc.publisherUniversity of Victoria
dc.subjectCanadian Rockies
dc.subjectmountains
dc.subjectecosystem change
dc.subjectimage enhancement
dc.subjectartificial intelligence
dc.subjectsemantic segmentation
dc.titleMountains of Confusion: Evaluating Image Enhancement to Improve AI Landscape Classification
dc.typePoster

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