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dc.contributor.authorAlhassan, Victor
dc.date.accessioned2018-10-29T19:48:19Z
dc.date.available2018-10-29T19:48:19Z
dc.date.issued2018-10-19
dc.identifier.citationAlhassan, Victor. Automated Land Use and Land Cover Map Production: A Deep Learning Framework. A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Department of Applied Computer Science, University of Winnipeg. Winnipeg, Manitoba, Canada: University of Winnipeg, 2018.en_US
dc.identifier.urihttp://hdl.handle.net/10680/1579
dc.description.abstractIn this thesis, we present an approach to automating the creation of land use and land cover (LULC) maps from satellite images using deep neural networks that were developed to perform semantic segmentation of natural images. This work is important since the production of accurate and timely LULC maps is becoming essential to government and private companies that rely on them for large-scale monitoring of land resource changes. In this work, deep neural networks are trained to classify each pixel of a satellite image into one of a number of LULC classes. The presented deep neural networks are all pre-trained using the ImageNet Large-Scale Visual Recognition Competition (ILSVRC) datasets and then fine-tuned using ~19,000 Landsat 5/7 satellite images of resolution 224 x 224 taken of the Province of Manitoba in Canada. The initial results achieved was 88% global accuracy. Furthermore, we consider the use of state-of-the-art generative adversarial architecture and context module to improve accuracy. The result is an automated deep learning framework that can produce LULC maps images significantly faster than current semi-automated methods. The contributions of this thesis are the observation that deep neural networks developed for semantic segmentation can be used to automate the task of producing LULC maps; extensive experimentation of different FCN architectures with extensions on a unique dataset; high classification accuracy of 90.46%; and a thorough analysis and accuracy assessment of our results.en_US
dc.language.isoenen_US
dc.publisherUniversity of Winnipeg
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDeep Learningen_US
dc.subjectLand useen_US
dc.subjectLand coveren_US
dc.subjectMapsen_US
dc.subjectClassificationen_US
dc.subjectDeep Convolutional Neural Networksen_US
dc.subjectSatellite imagesen_US
dc.titleAutomated Land Use and Land Cover Map Production: A Deep Learning Frameworken_US
dc.typeThesisen_US
dc.description.degreeMaster of Science in Applied Computer Scienceen_US
dc.publisher.grantorUniversity of Winnipegen_US
thesis.degree.disciplineMaster of Science in Applied Computer Science
thesis.degree.levelmasters
thesis.degree.nameMaster of Science in Applied Computer Science
thesis.degree.grantorUniversity of Winnipeg


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