Automated Building Extraction from Remote Sensing Imagery Using Deep Learning

Yan, Hailun

Automated Building Extraction from Remote Sensing Imagery Using Deep Learning

dc.contributor.advisor	Wang, Ruisheng
dc.contributor.author	Yan, Hailun
dc.contributor.committeemember	Bayat, Sayeh
dc.contributor.committeemember	Hay, Geoffrey
dc.date	2023-02
dc.date.accessioned	2022-11-03T15:48:29Z
dc.date.available	2022-11-03T15:48:29Z
dc.date.issued	2022-10-31
dc.description.abstract	Automatically extracting high-quality building-footprint polygons from satellite and aerial images is crucial for supporting various land use and land cover mapping applications. The conventional building polygon extraction process requires hand-crafted features and high human intervention, which is time-consuming and often has limited generalization capability. In recent years, deep learning-based methods have shed light on the problem with higher levels of automation, segmentation quality, and generalization capability. These methods often involve two stages: first, building segmentations are predicted from remote sensing images using deep neural networks; next, the irregular-shaped building segmentations are regularized into straight-edged and right-angle-cornered building polygons using conventional or deep learning-based methods. As a result, the extraction performance is often highly affected by the quality of the segmentation predictions. However, from experiments, the current widely used segmentation DNNs show significant defects in their building segmentation results, especially for the buildings with rotated angles between the building edges and the image edges. Moreover, although DNN-based regularization methods have shown greater generalization potentials at regularizing buildings in various shapes compared to the conventional regularization methods, the qualities of the regularization results are generally dissatisfactory. This thesis proposes an end-to-end deep learning-based building extraction method based on PolygonCNN. The proposed model consists of a segmentation module to predict building segmentations and a regularization module to regularize the building contours traced from the building segmentation results. First, an upgraded Mask R-CNN model, which is integrated with the rotatable bounding box technique, the Swin Transformer backbone network, and the FPN module, is adopted as the segmentation module of the proposed model to segment buildings in vastly different scales and orientations. Moreover, the Feature Pooling module and the BRegNet of the original PolygonCNN are modified to exploit the multi-scale feature maps of the FPN module. As a result, the proposed model can effectively extract high-quality building polygons with various scales and orientations and has shown promising performance compared to several other popular end-to-end deep-learning-based building extraction models. In addition, the thesis provides supplemental architecture choices, which offer flexibility between the quality of the building extraction result and the memory consumption of the model.	en_US
dc.identifier.citation	Yan, H. (2022). Automated building extraction from remote sensing imagery using deep learning (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.uri	http://hdl.handle.net/1880/115403
dc.identifier.uri	https://dx.doi.org/10.11575/PRISM/40384
dc.language.iso	eng	en_US
dc.publisher.faculty	Schulich School of Engineering	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	deep learning	en_US
dc.subject	end-to-end	en_US
dc.subject	building footprint extraction	en_US
dc.subject	remote sensing imagery	en_US
dc.subject	automatic	en_US
dc.subject.classification	Artificial Intelligence	en_US
dc.subject.classification	Engineering--Automotive	en_US
dc.subject.classification	Engineering--Civil	en_US
dc.subject.classification	Engineering--Industrial	en_US
dc.title	Automated Building Extraction from Remote Sensing Imagery Using Deep Learning	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Engineering – Geomatics	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Master of Science (MSc)	en_US
ucalgary.item.requestcopy	true	en_US