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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Environment and Planning B: Urban Analytics and City Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
