现有实证数据难以充分揭示大城市中人们居住与工作地点的分布特征；然而，街区层面的信息（如街景影像）却丰富且易获取。本研究构建了一种基于ResNet-50的社交检测模型，探索街景影像与职住属性之间的潜在关联。该方法提取某一街区八个方向的街景影像，用于预测地块的职住属性，并以熵指数衡量深圳市职住混合程度作为案例分析。社交检测模型在识别职住模式方面表现良好，均方根误差较低（RMSE = 0.1094）。相较于其他街区范围方法，八方向街区方法能更充分地利用街景影像信息，其RMSE为0.1135，表现最优。研究表明，结合街景影像与深度学习技术可有效表征与经济社会数据研究结果一致的职住属性特征；例如，研究发现深圳存在大量高职住混合区域，而专门用于就业或居住的区域极少。该方法若定期应用，可助力监测城市职住模式的空间动态变化，为城市规划与发展提供支持。

Empirical data are limited to decipher where people live and work in large cities; however, neighborhood information, such as street view image, is rich and abundant. We construct a ResNet-50-based social detection model to explore the potential relationship between street view images and job-housing attributes. The method extracts street view images of a neighborhood in all eight directions to predict land parcels’ job-housing attributes and uses an entropy index to measure the degree of job-housing mixture in Shenzhen as an example. The social-detection model performs well with a low RMSE (0.1094) in identifying job-housing patterns. The eight-direction neighborhood method shows the best support for sufficient neighborhood information from street view images (RMSE = 0.1135) compared with other neighborhood methods. This study demonstrates the feasibility of using street-view images and deep learning to characterize job-housing attributes consistent with findings from urban studies with socioeconomic data; for example, the research finding concurs that Shenzhen has many high job-housing mixtures with very few areas designated for jobs or residences. The proposed method, when applied regularly, can help monitor spatial dynamics of urban job-housing patterns to inform city planning and development.