摘要 城市感知是当前城市研究的热点话题，在城市规划与设计中发挥着积极作用。目前，计算城市感知主要有两种方法：1）利用模型直接自动学习图像特征；2）结合机器学习与基于专家知识的特征提取方法（如物体比例）。以武汉市两条典型街道为研究区域，采集视频数据作为模型输入。本研究选取两种代表性方法：1）端到端卷积神经网络（基于CNN的模型）；2）基于全卷积神经网络与随机森林（FCN + RF的模型）。通过对比两种模型的精度，分析其在不同城市场景下的适应性。同时，基于POI数据和OSM数据，分析基于CNN的模型与城市功能之间的关系，验证其可解释性。结果表明，基于CNN的模型精度高于FCN + RF的模型。由于基于CNN的模型考虑了地物的拓扑特征，其感知结果与城市功能之间具有更强的非线性相关性。此外，研究发现基于CNN的模型更适用于空间异质性较弱的场景（如中小城市环境），而FCN + RF的模型则适用于空间异质性较强的场景（如中国特大城市的中心城区）。本研究结果可为城市规划中的城市感知模型选择提供决策支持参考。

Abstract Urban perception is a hot topic in current urban study and plays a positive role in urban planning and design. At present, there are two methods to calculate urban perception. 1) Using a model to learn image features directly automatically; 2) Coupling machine learning and feature extraction based on expert knowledge (e.g. object proportion) method. With two typical streets in Wuhan as the study area, video data were recorded and used as the model input. In this study, two representative methods are selected: 1) End to end convolution neural network (CNN-based model); 2) Based on full convolution neural network and random forest (FCN + RF-based model). By comparing the accuracy of two models, we analyze the adaptability of the model in different urban scenes. We also analyze the relationship between CNN-based model and urban function based on POI data and OSM data, and verify its interpretability. The results show that the CNN-based model is more accurate than FCN + RF-based model. Because the CNN-based model considers the topological characteristics of the ground objects, its perception results have a stronger nonlinear correlation with urban functions. In addition, we also find that the CNN-based model is more suitable for scenes with weak spatial heterogeneity (such as small and medium-sized urban environments), while the FCN + RF-based model is applicable to scenes with strong spatial heterogeneity (such as the downtown areas of China’s megacities). The results of this study can be used as a reference to provide decision support for urban perception model selection in urban planning.