Automatic Mapping of Residential Rooftops with High-Resolution Thermal Imagery

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dc.contributor.advisorHay, Geoffrey J.
dc.contributor.authorGhaffarian, Salar
dc.contributor.committeememberYackel, John
dc.contributor.committeememberWang, Ruisheng
dc.contributor.committeememberHay, Geoffrey J.;
dc.dateSpring Convocation
dc.date.accessioned2023-05-11T05:36:35Z
dc.date.embargolift2024-04-25
dc.date.issued2022-04-25
dc.description.abstractThis study reports on the use of the commercially available ENVI Deep Learning module to (i) automatically extract GIS ready rooftop polygons directly from high-resolution night-time thermal infrared (TIR) airborne imagery and (ii) define the optimal spatial resolution for deep learning rooftop delineation. It also (iii) compares results from multi-spatial resolution models based on a single TIR image vs. a derived three channel image and (iv) introduces two new object-based accuracy assessment methods for comparing the visual fit of the segmented rooftops.
dc.identifier.citationGhaffarian, S. (2022). Automatic Mapping of Residential Rooftops with High-Resolution Thermal Imagery (Master thesis). University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca .
dc.identifier.urihttp://hdl.handle.net/1880/116438
dc.identifier.urihttps://dx.doi.org/10.11575/PRISM/dspace/41282
dc.language.isoEnglish
dc.publisher.facultyScience
dc.subjectAerial Thermal Imagery
dc.subjectSemantic Segmentation
dc.subjectBuilding Extraction
dc.subjectObject-based Image Analysis
dc.subject.classificationEarth Sciences
dc.titleAutomatic Mapping of Residential Rooftops with High-Resolution Thermal Imagery
dc.typemaster thesis
thesis.degree.disciplineGeography
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
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