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

arXiv: https://arxiv.org/abs/2112.14699 · HTML: https://mlsanity.com/paper/2112.14699/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 65 references that are in the map.
- First posted on arXiv 26 Dec 2021.

## Builds on (most cited first, 26 of 26)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.03242/) (2016)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Image reconstruction by domain transform manifold learning](https://mlsanity.com/paper/1704.08841/) (2017)
- [Linearly-Recurrent Autoencoder Networks for Learning Dynamics](https://mlsanity.com/paper/1712.01378/) (2017)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation](https://mlsanity.com/paper/2003.06988/) (2020)
- [Synthesizing Programs for Images using Reinforced Adversarial Learning](https://mlsanity.com/paper/1804.01118/) (2018)
- [RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion](https://mlsanity.com/paper/1904.12304/) (2019)
- [Spatio-Temporal Data Mining: A Survey of Problems and Methods](https://mlsanity.com/paper/1711.04710/) (2017)
- [Semi-supervised Learning on Graphs with Generative Adversarial Nets](https://mlsanity.com/paper/1809.00130/) (2018)
- [Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective](https://mlsanity.com/paper/2101.02634/) (2021)
- [Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models](https://mlsanity.com/paper/1702.06228/) (2017)
- [Reimagining City Configuration: Automated Urban Planning via Adversarial Learning](https://mlsanity.com/paper/2008.09912/) (2020)
- [CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generation](https://mlsanity.com/paper/1911.06641/) (2019)
- [Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning](https://mlsanity.com/paper/2110.07717/) (2021)
- [Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection](https://mlsanity.com/paper/2008.12618/) (2020)
- [DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction](https://mlsanity.com/paper/2009.00096/) (2020)
- [Modeling urbanization patterns with generative adversarial networks](https://mlsanity.com/paper/1801.02710/) (2018)
- [City-GAN: Learning architectural styles using a custom Conditional GAN architecture](https://mlsanity.com/paper/1907.05280/) (2019)
- [Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning](https://mlsanity.com/paper/2109.11053/) (2021)

## Built on by (most cited first, 11 of 11)

- [Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning](https://mlsanity.com/paper/2212.00904/) (2022)
- [Generative AI Meets Future Cities: Towards an Era of Autonomous Urban Intelligence](https://mlsanity.com/paper/2304.03892/) (2023)
- [Road Planning for Slums via Deep Reinforcement Learning](https://mlsanity.com/paper/2305.13060/) (2023)
- [Generative AI for Urban Planning: Synthesizing Satellite Imagery via Diffusion Models](https://mlsanity.com/paper/2505.08833/) (2025)
- [Towards Urban Planing AI Agent in the Age of Agentic AI](https://mlsanity.com/paper/2507.14730/) (2025)
- [Automated Floodwater Depth Estimation Using Large Multimodal Model for Rapid Flood Mapping](https://mlsanity.com/paper/2402.16684/) (2024)
- [Dual-stage Flows-based Generative Modeling for Traceable Urban Planning](https://mlsanity.com/paper/2310.02453/) (2023)
- [City Editing: Hierarchical Agentic Execution for Dependency-Aware Urban Geospatial Modification](https://mlsanity.com/paper/2602.19326/) (2026)
- [Automated Urban Planning aware Spatial Hierarchies and Human Instructions](https://mlsanity.com/paper/2209.13002/) (2022)
- [Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations](https://mlsanity.com/paper/2212.12932/) (2022)
- [Mixture Experts with Test-Time Self-Supervised Aggregation for Tabular Imbalanced Regression](https://mlsanity.com/paper/2506.07033/) (2025)

## 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.