1 citations · 1 across the 3 of their papers we have counts for
5 papers
Re-designing cities with conditional adversarial networks
Mohamed R. Ibrahim, James Haworth, Nicola Christie
This paper introduces a conditional generative adversarial network to redesign a street-level image of urban scenes by generating 1) an urban intervention policy, 2) an attention m…
CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning
Mohamed R. Ibrahim, James Haworth, Nicola Christie +1
Cycling is a promising sustainable mode for commuting and leisure in cities, however, the fear of getting hit or fall reduces its wide expansion as a commuting mode. In this paper,…
WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning
Mohamed R. Ibrahim, James Haworth, Tao Cheng
Extracting information related to weather and visual conditions at a given time and space is indispensable for scene awareness, which strongly impacts our behaviours, from simply w…
URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision
Mohamed R. Ibrahim, James Haworth, Tao Cheng
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 a…
predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning
Mohamed R. Ibrahim, Helena Titheridge, Tao Cheng +1
Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifyi…