Browsing by Author "Suliman, Alaelidn Muhmud Housat"

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    A novel maturity-based assessment model for smart cities
    (University of New Brunswick, 2019) Suliman, Alaelidn Muhmud Housat; Rankin, Jeff; Robak, Anna
    As a response to the challenges of population and urban growth, the concept of smart city/community (SC) is introduced as a strategic solution to the traditional city-related problems to achieve better services/life quality. However, the SC as an ecosystem is an evolving concept; hence, there is no universally-shared definition or assessment tool. SC assessment models broadly fall into two categories: performance-based and maturity-based models. Most of the available assessment models are based on performance indicators. However, unlike maturity-based models, performance indicators face challenges due to the complexity and evolving nature of SCs. Therefore, this research addresses the problems of a universally-accepted definition of SCs and assessment framework by (1) identifying the key smartness dimensions of a city, (2) building a corresponding novel smartness concept, and (3) developing a full maturity-based assessment model that overcomes the limitations of the performance-based models. The research contribution includes the identification of three key dimensions for SCs, which are Connectivity (C), Sustainability (S), and Resiliency (R); and a corresponding maturity-based assessment model (MM) for SCs referred to as CSR-MM. The applicability of CSR-MM was demonstrated through (1) examining its conformance to the MM design principles, and (2) demonstrating its practically via (a) a sub-domain case study (Fredericton Public Transit, NB) and (b) an outcome comparison against an international assessment tool (ISO37120:2018). The outcome of this study is an SC assessment model that is intended to help municipalities to identify maturity gaps, set prioritized goals, and focus on continuously improving citizens’ well-being.
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    Building detection in off-nidar very high resolution satellite images based on stereo 3D information
    (University of New Brunswick, 2017) Suliman, Alaelidn Muhmud Housat; Zhang, Yun
    Mapping or updating maps of urban areas is crucial for urban planning and management. Since buildings are the main objects in urban environments, building roof detection is an important task in urban mapping. The ideal geo-spatial data source for mapping building information is very high resolution (VHR) satellite images. On the other hand, because buildings are elevated objects, incorporating their heights in building detection can significantly improve the accuracy of the mapping. The most cost-effective source for extracting the height information is stereo VHR satellite images that can provide two types of stereo 3D information: elevation and disparity. However, most VHR images are acquired off-nadir. This acquisition type causes building leaning in the images and creates major challenges for the incorporation of building height information into roof detection. Thus, this PhD research focuses on finding solutions to mitigate the problems associated with 3D-supported building detection in off-nadir VHR satellite images. It also exploits the potential of extracting disparity information from off-nadir image pairs to support building detection. In the research, several problems associated with building leaning need to be solved, such as building roof offsetting from its footprint, object occlusion, and building façades. Moreover, the variation of the roofs offsets based on the building heights. While the offsets of building roof create difficulties in the co-registration between image and elevation data, the building façades and occlusions create challenges in automatically finding matching points in off-nadir image pairs. Furthermore, due to the variation in building-roof offsets, the mapped roofs extracted from off-nadir images cannot be directly geo-referenced to existing maps for effective information integration. In this PhD dissertation, all of the above identified problems are addressed in a progressively improving manner (i.e., solving the problems one after another while improving the efficiency) within the context of 3D-supported building detection in off-nadir VHR satellite images. Firstly, an image-elevation co-registration technique is developed that is more efficient than the currently available techniques. Secondly, the computation cost is then reduced by generating disparity information instead of the traditional elevation data. This allows bypassing a few time-consuming steps of the traditional method. Thirdly, the disparity generation is then extended from using one pair of off-nadir images to using multiple pairs for achieving an enriched disparity map. Finally, the enriched disparity maps achieved are then used to efficiently derive elevations that are directly co-registered with pixel-level accuracy to the selected reference image. Based on these disparity-based co-registered elevations, building roofs are successfully detected and accurately geo-referenced to existing maps. The outcome of this PhD research proved the possibility of using off-nadir VHR satellite images for accurate urban building detection. It significantly increases the data source scope for building detection since most (> 95%) of VHR satellite images are off-nadir and traditional methods cannot effectively handle off-nadir images.