尽管近年来已利用机器学习构建了全球覆盖的城市感知数据集，但其在准确评估其他国家和地区局部城市感知方面的有效性仍存在问题。本文描述了一种基于深度学习与迭代反馈及推荐评分相结合的方法，提出了一种人机对抗评分框架，可实现对中国城市局部城市感知的快速、低成本评估。采用先进的全卷积网络（FCN）和随机森林（RF）算法，该方法的感知估计误差低于10%。从视觉特征和城市功能两个方面进行驱动因素分析，验证了其在推导局部城市感知方面的可行性。该人机对抗框架具备高通量与高精度评分能力，为城市规划者和研究人员提供了一种经济、快速的局部城市感知评估解决方案。

Though global-coverage urban perception datasets have been recently created using machine learning, their efficacy in accurately assessing local urban perceptions for other countries and regions remains a problem. Here we describe a human-machine adversarial scoring framework using a methodology that incorporates deep learning and iterative feedback with recommendation scores, which allows for the rapid and cost-effective assessment of the local urban perceptions for Chinese cities. Using the state-of-the-art Fully Convolutional Network (FCN) and Random Forest (RF) algorithms, the proposed method provides perception estimations with errors less than 10%. The driving factor analysis from both the visual and urban functional aspects demonstrated its feasibility in facilitating local urban perception derivations. With high-throughput and high-accuracy scorings, the proposed human-machine adversarial framework offers an affordable and rapid solution for urban planners and researchers to conduct local urban perception assessments.