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Co-registration of very-high-resolution (VHR) images with elevation data is extremely important for many remote sensing applications due to the complementary properties of these two data types. However, this type of multidata source registration has many associated challenges. For instance, although VHR satellite images are usually acquired off-nadir, the integration of off-nadir images with digital surface models (DSMs) for the purpose of urban mapping has been rarely seen in research publications. This is due to the relief displacement of the elevated objects, which causes a problematic misregistration between the perspective off-nadir images and the corresponding orthographic DSMs. Therefore, the co-registration of such datasets is almost impossible unless a true orthorectification process is executed. However, true orthoimages are expensive, time consuming, and difficult to achieve. Thus, this paper proposes a registration method based on developing a line-of-sight DSM solution to effectively register elevation data with off-nadir VHR images. The method utilizes the relevant sensor model in two phases: deriving DSM from stereo images and reprojecting the DSM back to one of the stereo images to generate a line-of-sight DSM for accurate co-registration. To demonstrate the applicability of the proposed method and evaluate the effect of the misregistration, a building detection procedure is implemented. The proposed method is found to be feasible, inexpensive, and of subpixel accuracy. Additionally, it improves the overall accuracy of detecting buildings by almost 12% relative to that when the conventional two-dimensional (2-D) registration technique is used solely due to the elimination of the misregistration effect.

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ANNs (Artificial neural networks) are used extensively in remote sensing image processing. It has been proven that BPNNs (back-propagation neural networks) have high attainable classification accuracy. However, there is a noticeable variation in the achieved accuracies due to different network designs and implementations. Hence, researchers usually need to conduct several experimental trials before they can finalize the network design. This is a time consuming process which significantly reduces the effectiveness of using BPNNs and the final design may still not be optimal. Therefore, there is a need to see whether there are some common guidelines for effective design and implementation of BPNNs. With this aim in mind, this paper attempts to find and summarize the common guidelines suggested by different authors through literature review and discussion of the findings. To provide readers with background and contextual information, some ANN fundamentals are also introduced.

As a response to the population and urban growth challenges, the concept of smart city/community (SC) has been introduced as a strategic solution to the traditional city-related problems. This research seeks to identify the key smartness dimensions of a city, build a corresponding novel smartness concept, and develop a full assessment model. The contribution of this research includes identifying three key dimensions for SCs: Connectivity (C), Sustainability (S), and Resiliency (R); and developing a corresponding SC maturity-based assessment model (MM) referred to as CSR-MM. The model applicability is validated through examining its conformance to the MM design principles and practically demonstrated via a case study (Fredericton Public Transit, NB) with an outcome comparison against an international SC assessment tool (ISO37120:2018). The research significance is by providing CSR-MM that is intended to help municipalities to identify maturity gaps, set prioritized goals, and focus on continuously improving citizens’ well-being.