Urban planning refers to the efforts of designing land-use configurations. Effective urban planning can help to mitigate the operational and social vulnerability of a urban system, such as high tax, crimes, traffic congestion and accidents, pollution, depression, and anxiety. Due to the high complexity of urban systems, such tasks are mostly completed by professional planners. But, human planners take longer time. The recent advance of deep learning motivates us to ask: can machines learn at a human capability to automatically and quickly calculate land-use configuration, so human planners can finally adjust machine-generated plans for specific needs? To this end, we formulate the automated urban planning problem into a task of learning to configure land-uses, given the surrounding spatial contexts. To set up the task, we define a land-use configuration as a longitude-latitude-channel tensor, where each channel is a category of POIs and the value of an entry is the number of POIs. The objective is then to propose an adversarial learning framework that can automatically generate such tensor for an unplanned area. In particular, we first characterize the contexts of surrounding areas of an unplanned area by learning representations from spatial graphs using geographic and human mobility data. Second, we combine each unplanned area and its surrounding context representation as a tuple, and categorize all the tuples into positive (well-planned areas) and negative samples (poorly-planned areas). Third, we develop an adversarial land-use configuration approach, where the surrounding context representation is fed into a generator to generate a land-use configuration, and a discriminator learns to distinguish among positive and negative samples.
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）
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）
Collaborative Imputation of Urban Time Series through Cross-city Meta-learning
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University（香港理工大学土木与环境工程系）；Department of Traffic Engineering, Tongji University（同济大学交通工程系）；Centre for Learning, Teaching, and Technology, The Education University of Hong Kong（香港教育大学学习、教学与科技中心）；Research Institute for Artificial Intelligence of Things, Department of Computing, The Hong Kong Polytechnic University（香港理工大学人工智能物联网研究所）
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。
CityPlanner: A Sandbox Agent for Executable Urban Planning
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；MIIT Key Laboratory of Data and Decision Intelligence, Beihang University（北京航空航天大学工业和信息化部数据与决策智能重点实验室）
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）
CitySeeker: How Do VLMS Explore Embodied Urban Navigation With Implicit Human Needs?
Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）；Tongji University（同济大学）；Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences（中国科学院南京地理与湖泊研究所）；Research Centre for Data Science & Artificial Intelligence（数据科学与人工智能研究中心）
City Editing: Hierarchical Agentic Execution for Dependency-Aware Urban Geospatial Modification
University of Kansas, Lawrence, KS, USA（堪萨斯大学）；Seoul National University, Seoul, South Korea（首尔国立大学）；University of Florida, Gainesville, FL, USA（佛罗里达大学）；NEC Laboratories America, Princeton, NJ, USA（NEC美国实验室）；Clemson University, Clemson, SC, USA（克莱姆森大学）；Arizona State University, Tempe, AZ, USA（亚利桑那州立大学）
CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments
College of Systems Engineering, National University of Defense Technology（系统工程学院，国防科技大学）；State Key Laboratory of Digital Intelligent Modeling and Simulation（数字智能建模与仿真国家重点实验室）；BNRist, Tsinghua University（清华大学北京研究院）；Department of Electronic Engineering, Tsinghua University（电子工程系，清华大学）
