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.

在社交媒体和制图平台上，构建环境图像的在线文档库不断增长，提供了一种新型的构建环境档案。计算机视觉，特别是卷积神经网络的最新进展，提供了查询和分析大型图像语料库的新方法。在本文中，我们提出了一种新的方法，通过该方法，建筑环境的历史学家可以在研究中使用这些庞大的图像语料库，从而提出新的研究问题。为了证明需要的证据，我们在特拉维夫进行了一项正在进行的案例研究，该案例试图说明我们提出的方法的可行性，以使基于历史城市景观（HUL）的方法能够对建筑环境进行研究。通过这样做，我们展示了这种图像语料库如何潜在地形成一种用于建筑和城市历史的新型档案。