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In recent years, deep learning and computer vision have been applied to solve complex problems across many domains. In urban studies, these technologies have been instrumental in the development of smart cities and autonomous vehicles. However, a knowledge gap is present when it comes to informal urban regions in less developed countries. How can deep learning and artificial intelligence untangle the complexities of informality to advance urban modelling? In this paper, we introduce a framework for multipurpose realistic-dynamic urban modelling using deep convolutional neural networks. The purpose of the framework is twofold: (1) to sense and detect informality and slums in urban scenes from aerial and street-level images and (2) to detect pedestrian and transport modes. The model has been trained on images of urban scenes in cities across the globe. The framework shows strong validation performance in the identification of planned and unplanned regions, despite broad variations in the classified images. The algorithms of the URBAN-i model are coded in Python and the trained models can be applied to images of any urban setting, including informal settlements and slum regions.

AbstractThe present paper attempts to fill a part of the gap in the studies on residential location choices and their relations to urban mobility, socio-economics, and the built environment by presenting the results of a study on Alexandria, Egypt, by analysing the results of a survey in eight neighbourhoods undertaken in 2015. Four questions were answered in this study: (i) "How are the main drivers behind residential location choices in Alexandria connected to various socio-demographic groups or people with different availability to urban and built environments?", (ii) "How are the main residential self-selections in Alexandria associated with one another and which are the most important?", (iii) "How are the housing location-related decisions of Egyptians similar to or different from international decisions?", and (iv) "How can planners and decision-makers use the knowledge produced by this study for urban planning and housing in Egypt?". Library work and the results of a Χ2 test of independence show that availability of transportation modes, nice neighbourhoods, and affordability are the strongest motives behind decisions. However, socio-economic factors are generally stronger than urban mobility and spatial issues. These findings are partly different from those of high-income countries.