WinnSpace Repository

Automated Land Use and Land Cover Map Production: A Deep Learning Framework

Show simple item record

dc.contributor.author Alhassan, Victor
dc.date.accessioned 2018-10-29T19:48:19Z
dc.date.available 2018-10-29T19:48:19Z
dc.date.issued 2018-10-19
dc.identifier.citation Alhassan, 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, 2018. en_US
dc.identifier.uri http://hdl.handle.net/10680/1579
dc.description.abstract In 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.iso en en_US
dc.subject Deep Learning en_US
dc.subject Land use en_US
dc.subject Land cover en_US
dc.subject Maps en_US
dc.subject Classification en_US
dc.subject Deep Convolutional Neural Networks en_US
dc.subject Satellite images en_US
dc.title Automated Land Use and Land Cover Map Production: A Deep Learning Framework en_US
dc.type Thesis en_US
dc.description.degree Master of Science in the Department of Applied Computer Science en_US
dc.publisher.grantor University of Winnipeg en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search WinnSpace


Browse

My Account