Characterisation of urban environment and activity across space and time using street images and deep learning in Accra
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Scientific reports
Abstract
The urban environment influences human health, safety and wellbeing. Cities in Africa are growing
faster than other regions but have limited data to guide urban planning and policies. Our aim was
to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of
features of the urban environment relevant for health, liveability, safety and sustainability. We
collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative
locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images
for 20 contextually relevant objects and used transfer learning with data augmentation to retrain
a convolutional neural network to detect them in the remaining images. We identified 23.5 million
instances of these objects including 9.66 million instances of persons (41% of all objects), followed by
cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as
tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in
the commercial core and densely populated informal neighbourhoods, while refuse and animals were
most observed in the peripheries. The daily variability of objects was smallest in densely populated
settlements and largest in the commercial centre. Our novel data and methodology shows that
smart sensing and analytics can inform planning and policy decisions for making cities more liveable,
equitable, sustainable and healthy.
Description
Research Article
Keywords
urban environment, human health, safety, wellbeing