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dc.contributor.advisorKampffmeyer, Michael
dc.contributor.authorSalomonsen, Christian
dc.date.accessioned2024-08-07T05:41:11Z
dc.date.available2024-08-07T05:41:11Z
dc.date.issued2024-06-01en
dc.description.abstractEnhanced accuracy of building detection algorithms has the potential to benefit a wide array of applications, including urban planning, environmental monitoring, and disaster response efforts. However, building extraction algorithms struggle with robustness due to among others, occlusions from vegetation and shadows of nearby tall buildings, complex building shapes, and a large distributional shift between datasets that come in varying spatial resolutions, resulting in their dependency of dataset-specific and user specified parameters. Towards addressing this shortcoming, we hypothesize that the model's uncertainty can be leveraged to increase the robustness and efficacy of these algorithms. As a first step towards evaluating this hypothesis, we propose an improved version of the current state-of-the-art that incorporates quantification of the model uncertainty. We further show that leveraging these uncertainty methods by guiding the vertex selection process through the use of a dynamic threshold improves the stability across datasets. Results on two datasets demonstrate that incorporating uncertainty has the potential to significantly improve the robustness of the previous state-of-the-art method. Additionally, the dynamic threshold, while offering a more modest improvement, showcases the potential of actively leveraging uncertainty measures to improve the model performance.en_US
dc.identifier.urihttps://hdl.handle.net/10037/34200
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universitetno
dc.publisherUiT The Arctic University of Norwayen
dc.rights.holderCopyright 2024 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)en_US
dc.subject.courseIDFYS-3941
dc.titleUncertainty Guided Polygon Generation for Building Detectionen_US
dc.typeMastergradsoppgaveno
dc.typeMaster thesisen


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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)