32 citations · 36 across the 6 of their papers we have counts for
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cs.CV2018★ 32 cited
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…
cs.CY2018★ 1 cited
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…