mlsanityData to 7 Oct 2026

Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation

arXiv 2112.14699 ↗First posted 26 Dec 2021cs.RO cs.AIcited by 11

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Abstract (arXiv)

Urban planning refers to the efforts of designing land-use configurations given a region. However, to obtain effective urban plans, urban experts have to spend much time and effort analyzing sophisticated planning constraints based on domain knowledge and personal experiences. To alleviate the heavy burden of them and produce consistent urban plans, we want to ask that can AI accelerate the urban planning process, so that human planners only adjust generated configurations for specific needs? The recent advance of deep generative models provides a possible answer, which inspires us to automate urban planning from an adversarial learning perspective. However, three major challenges arise: 1) how to define a quantitative land-use configuration? 2) how to automate configuration planning? 3) how to evaluate the quality of a generated configuration? In this paper, we systematically address the three challenges. Specifically, 1) We define a land-use configuration as a longitude-latitude-channel tensor. 2) We formulate the automated urban planning problem into a task of deep generative learning. The objective is to generate a configuration tensor given the surrounding contexts of a target region. 3) We provide quantitative evaluation metrics and conduct extensive experiments to demonstrate the effectiveness of our framework.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 26 of its 65 references that are in the map. First posted on arXiv 26 Dec 2021.

Builds on

26 of 65 references.

References · 26

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14 more
  1. 2018Synthesizing Programs for Images using Reinforced Adversarial Learningcited by 72
  2. 2019RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completioncited by 58
  3. 2017Spatio-Temporal Data Mining: A Survey of Problems and Methodscited by 29
  4. 2018Semi-supervised Learning on Graphs with Generative Adversarial Netscited by 21
  5. 2021Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspectivecited by 21
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  7. 2020Reimagining City Configuration: Automated Urban Planning via Adversarial Learningcited by 18
  8. 2019CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generationcited by 16
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 1; 2025: 3; 2026: 1.

11 papers.

  1. 2022Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planningcited by 15
  2. 2023Generative AI Meets Future Cities: Towards an Era of Autonomous Urban Intelligencecited by 8
  3. 2023Road Planning for Slums via Deep Reinforcement Learningcited by 7
  4. 2025Generative AI for Urban Planning: Synthesizing Satellite Imagery via Diffusion Modelscited by 7
  5. 2025Towards Urban Planing AI Agent in the Age of Agentic AIcited by 5
  6. 2024Automated Floodwater Depth Estimation Using Large Multimodal Model for Rapid Flood Mappingcited by 4
  7. 2023Dual-stage Flows-based Generative Modeling for Traceable Urban Planningcited by 2
  8. 2022Automated Urban Planning aware Spatial Hierarchies and Human Instructionscited by 1
  9. 2026City Editing: Hierarchical Agentic Execution for Dependency-Aware Urban Geospatial Modificationcited by 1
  10. 2022Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlationscited by 0
  11. 2025Mixture Experts with Test-Time Self-Supervised Aggregation for Tabular Imbalanced Regressioncited by 0