Using deep learning and street-level imagery to predict social and political attributes

在过去的五年里，机器学习最有趣的应用之一是情感计算领域。这个应用机器学习的领域使用计算机视觉技术来预测人类对他们所看到的图像的感觉。当应用于街道和卫星图像时，情感计算为我们提供了一种尝试理解建筑环境如何让我们感觉或行动的方法。正如欧文在他的声明中指出的那样，这些视觉线索已被广泛用于构建“街道感觉”模型，这些模型根据街道的外观来预测给定地方的社会和经济结果。欧文在他的文献综述中讨论了其中的一些，例如Gebru等人（2017）基于街头停放的汽车类型对2008年总统大选的预测。在地理学中，Alexiou等人（2016）制作的城市形态多维开放数据（MODUM）分类旨在基于城市结构和形态数据进行通用分类。欧文的论文旨在通过街道和卫星层面的图像构建一个地方的外观和感觉来做到这一点。这不仅仅是一个小把戏：使用街道层面的图像预测政治信息的能力使我们能够真正感受到物理环境对社会进程的重要性，并为“现在铸造”重要的社会和政治特征打开大门。从这个意义上说，街道级计算机视觉模型真正扩大了社会科学的范围。然而，需要进一步的工作来确定这些预测是否可靠。这是欧文提议的工作。

In the last five years, one of the most interesting applications of machine learning has been in the area of affective computing. This domain of applied machine learning has used computer vision techniques to predict how humans feel about images they see. When applied to street- level and satellite imagery, affective computing gives us a method to try to understand how the built environment makes us feel or act. As Owen notes in his statement, these visual cues have been used extensively to build "feel-of-a-street" models that predict social and economic outcomes for a given place based on how the place looks from the street. Owen discusses a few of these in his literature review, such as Gebru et al. (2017)'s predictions of the 2008 presidential election based on the types of street-parked cars. In geography, the classification of Multidimensional Open Data of Urban Morphology (MODUM) produced by Alexiou et al. (2016) aimed for a general-purpose classification based on data about urban structure and morphology. Owen's dissertation aims to do this through the look and feel of a place, constructed from street and satellite-level imagery.This is not just a parlor trick: the ability to predict political information using street-level imagery allows us to really get a feel for how physical context matters for social processes, as well as opening the door into "now-casting" important social and political traits. In this sense, street-level computer vision models have truly expanded the reach of social science. However, further work is needed to determine whether these predictions are reliable. This is the work Owen proposes.