SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted Machine Learning for Improved Detection and Segmentation of Building Boundary(Cardiff University, 2022-09-27) Algarni, Salem; Mourshed, MonjurThis thesis addresses the need for rapid assessment of damaged assets, such as buildings, following natural or man-made disasters. Traditional manual visual analysis is labor-intensive and time-consuming, prompting the exploration of automated detection methods using multi-source geospatial data. The research reviews existing object detection methods and introduces two novel post-processing techniques. Artificial intelligence, particularly convolutional neural networks (CNNs), is employed to improve building detection accuracy. The study emphasizes the superior performance of CNN architectures, especially Region-based Convolutional Neural Network (Mask R-CNN), which enhances semantic detection and boundary recognition. Mask R-CNN, combined with conditional random fields (CRFs), effectively identifies and refines building contours in satellite images. The proposed post-processing techniques modify the relative orientation properties of buildings and integrate key points from two neural networks to adjust predicted contours using innovative snap algorithms. The results demonstrate notable improvements in boundary detection accuracy, with enhancements of 2.5% in F1-Score, 4.6% in Intersection over Union, and 1% in overall pixel accuracy compared to current state-of-the-art methods. The thesis highlights the potential of CNNs in automating image processing tasks, learning complex concepts from raw data, and aiding infrastructure planning and disaster response.39 0