Nowadays, the smart city development levels of different cities are still unbalanced. For a large number of cities which just started development, the governments will face a critical cold-start problem: 'how to develop a new smart city service with limited data?'. To address this problem, transfer learning can be leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm. This article investigates the common process of urban transfer learning, aiming to provide city planners and relevant practitioners with guidelines on how to apply this novel learning paradigm. Our guidelines include common transfer strategies to take, general steps to follow, and case studies in public safety, transportation management, etc. We also summarize a few research opportunities and expect this article can attract more researchers to study urban transfer learning.
A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；JD iCity, JD Technology（京东科技京东智联云）；JD Intelligent Cities Research（京东智能城市研究院）；Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology（香港科技大学土木与环境工程系）
CITYREP: A Unified Benchmark for Urban Representations Across Cities, Tasks, and Modalities
SpaceTimeLab, University College London, UK（伦敦大学空间时间实验室）；DIMPact, University College London, UK（伦敦大学3DIMPact实验室）；School of Resource and Environmental Sciences, Wuhan University, China（武汉大学资源与环境科学学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China（武汉大学测绘遥感信息工程国家重点实验室）
UrbanVerse: Learning Urban Region Representation Across Cities and Tasks
School of Computing and Information System, University of Melbourne（计算机与信息系统学院，墨尔本大学）；School of Computer Science and Engineering, University of New South Wales（计算机科学与工程学院，新南威尔士大学）
Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement
Oak Ridge National Laboratory（橡树岭国家实验室）；University of Central Florida（中佛罗里达大学）；UNSW Sydney（新南威尔士大学悉尼分校）；University of Oxford（牛津大学）；Curtin University（科廷大学）
Urban precipitation downscaling using deep learning: a smart city application over Austin, Texas, USA
Jackson School of Geosciences（杰克逊地球科学学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；CIRES, University of Colorado Boulder（科罗拉多大学博尔德分校环境科学研究合作研究所）；NOAA/Physical Sciences Laboratory（美国国家海洋和大气管理局物理科学实验室）；Purdue University（普渡大学）；Cockrell School of Engineering（科克雷尔工程学院）
城市尺度缩减是将知识从较粗糙的气候信息转移到城市尺度评估的一个环节。这些高分辨率的评估需要过去数据和未来预测的多年气候学，使用传统的数值天气预测模型生成这些数据是复杂的，计算成本高。美国得克萨斯州奥斯汀市在过去十年中取得了巨大的发展。未来的系统规划需要高分辨率的城市规模数据集。在这项研究中，我们展示了一种新的方法，使用深度学习来生成一个通用操作符来执行城市降尺度。该算法在美国德克萨斯州奥斯汀市使用迭代超分辨率卷积神经网络（Iterative SRCNN）。我们展示了一个高分辨率的网格化降水产品（300米）从一个粗糙的（10公里）基于卫星的产品（JAXA GsMAP）的发展。高分辨率网格化的降水数据集提供了对过去大到小降水事件空间发布分布的深入了解。该算法在平均峰值信噪比和互信息方面有所改善，相对于三次插值基线，该算法可以生成尺寸为300 m X 300 m的高分辨率网格积。我们的研究结果对开发高分辨率网格化降水城市数据集以及未来针对其他城市和其他气候变量的智能城市规划具有重要意义。
Bi-directional Mapping of Morphology Metrics and 3D City Blocks for Enhanced Characterization and Generation of Urban Form
Singapore-ETH Centre, Future Cities Lab Global Programme（新加坡-ETH中心，未来城市实验室全球计划）；School of Architecture, Southeast University（东南大学建筑学院）；Department of Architecture, Swiss Federal Institute of Technology Zurich（瑞士联邦理工学院苏黎世分校建筑系）；Department of Architecture, National University of Singapore（新加坡国立大学建筑系）；Department of Real Estate, National University of Singapore（新加坡国立大学房地产系）
City electric power consumption forecasting based on big data & neural network under smart grid background
School of Computer Science & Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；University of Nottingham（诺丁汉大学）；School of Aeronautics and Astronautics, Tsinghua University（清华大学航空航天学院）
