Towards a new image archive for the built environment

Kartikeya Date and Yael Allweil

Environment and Planning B, 2022, vol. 49, issue 2, 519-534

Abstract: The ever-growing online corpus of images of the built environment, on social media and mapping platforms, offers a new kind of archive of the built environment. Recent advances in computer vision, specifically convolutional neural networks, offer new ways of querying and analyzing large image corpuses. In this paper, we propose a new method by which historians of the built environment can use these vast image corpuses in their study, enabling new research questions. To demonstrate proof of need, we report on an ongoing case study in Tel Aviv that attempts to show the feasibility of our proposed method for enabling a Historic Urban Landscapes (HUL)-based approach to the study of the built environment. In so doing, we show how such image corpuses could potentially form a new type of archive for architectural and urban history.

Keywords: Archives; built environment; neural networks; architectural history; methods (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:49:y:2022:i:2:p:519-534

DOI: 10.1177/23998083211011474

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