  Within the burgeoning expansion of deep learning and computer vision across
the different fields of science, when it comes to urban development, deep
learning and computer vision applications are still limited towards the notions
of smart cities and autonomous vehicles. Indeed, a wide gap of knowledge
appears when it comes to cities and urban regions in less developed countries
where the chaos of informality is the dominant scheme. How can deep learning
and Artificial Intelligence (AI) untangle the complexities of informality to
advance urban modelling and our understanding of cities? Various questions and
debates can be raised concerning the future of cities of the North and the
South in the paradigm of AI and computer vision. In this paper, we introduce a
new method for multipurpose realistic-dynamic urban modelling relying on deep
learning and computer vision, using deep Convolutional Neural Networks (CNN),
to sense and detect informality and slums in urban scenes from aerial and
street view images in addition to detection of pedestrian and transport modes.
The model has been trained on images of urban scenes in cities across the
globe. The model shows a good validation of understanding a wide spectrum of
nuances among the planned and the unplanned regions, including informal and
slum areas. We attempt to advance urban modelling for better understanding the
dynamics of city developments. We also aim to exemplify the significant impacts
of AI in cities beyond how smart cities are discussed and perceived in the
mainstream. The algorithms of the URBAN-i model are fully-coded in Python
programming with the pre-trained deep learning models to be used as a tool for
mapping and city modelling in the various corner of the globe, including
informal settlements and slum regions.
