Characterisation of urban environment and activity across space and time using street images and deep learning in Accra

Simple item page
dc.contributor.authorNathvani, R.
dc.contributor.authorClark, S.N.
dc.contributor.authorMuller, E.
dc.contributor.authorAlli, A.S.
dc.contributor.authorBennett, J.E.
dc.contributor.authorNimo, J.
dc.contributor.authorMoses, J.B.
dc.contributor.authorBaah, S.
dc.contributor.authorMetzler, A.B.
dc.contributor.authorBrauer, M.
dc.contributor.authorSuel, E.
dc.contributor.authorHughes, A.F.
dc.contributor.authorAgyemang, E.
dc.contributor.authorOwusu, G.
dc.contributor.authorAgyei‑Mensah, S.
dc.date.accessioned2023-01-20T13:09:46Z
dc.date.available2023-01-20T13:09:46Z
dc.date.issued2022
dc.descriptionResearch Articleen_US
dc.description.abstractThe 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.en_US
dc.identifier.otherhttps://doi.org/10.1038/s41598-022-24474-1
dc.identifier.urihttp://ugspace.ug.edu.gh:8080/handle/123456789/38427
dc.language.isoenen_US
dc.publisherScientific reportsen_US
dc.subjecturban environmenten_US
dc.subjecthuman healthen_US
dc.subjectsafetyen_US
dc.subjectwellbeingen_US
dc.titleCharacterisation of urban environment and activity across space and time using street images and deep learning in Accraen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Characterisation-of-urban-environment-and-activity-across-space-and-time-using-street-images-and-deep-learning-in-AccraScientific-Reports.pdf
Size:
5.31 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Department of Physics