# Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning

arXiv: https://arxiv.org/abs/2110.07717 · HTML: https://mlsanity.com/paper/2110.07717/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [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)
- [S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation](https://mlsanity.com/paper/2005.11437/) (2020)
- [Guided Variational Autoencoder for Disentanglement Learning](https://mlsanity.com/paper/2004.01255/) (2020)
- [Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective](https://mlsanity.com/paper/2101.02634/) (2021)
- [Reimagining City Configuration: Automated Urban Planning via Adversarial Learning](https://mlsanity.com/paper/2008.09912/) (2020)
- [Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection](https://mlsanity.com/paper/2008.12618/) (2020)
- [MG-VAE: Deep Chinese Folk Songs Generation with Specific Regional Style](https://mlsanity.com/paper/1909.13287/) (2019)
- [Semi-Recurrent CNN-based VAE-GAN for Sequential Data Generation](https://mlsanity.com/paper/1806.00509/) (2018)
- [Disentangling the Spatial Structure and Style in Conditional VAE](https://mlsanity.com/paper/1910.13062/) (2019)

## Built on by (most cited first, 13 of 13)

- [Large Language Model for Participatory Urban Planning](https://mlsanity.com/paper/2402.17161/) (2024)
- [Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook](https://mlsanity.com/paper/2402.19348/) (2024)
- [Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning](https://mlsanity.com/paper/2212.00904/) (2022)
- [Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation](https://mlsanity.com/paper/2112.14699/) (2021)
- [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)
- [Large language model empowered participatory urban planning](https://mlsanity.com/paper/2402.01698/) (2024)
- [Towards Urban Planing AI Agent in the Age of Agentic AI](https://mlsanity.com/paper/2507.14730/) (2025)
- [Generative AI for Urban Design: A Stepwise Approach Integrating Human Expertise with Multimodal Diffusion Models](https://mlsanity.com/paper/2505.24260/) (2025)
- [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)
- [Climate-sensitive Urban Planning through Optimization of Tree Placements](https://mlsanity.com/paper/2310.05691/) (2023)

## Abstract (arXiv)

Urban planning designs land-use configurations and can benefit building livable, sustainable, safe communities. Inspired by image generation, deep urban planning aims to leverage deep learning to generate land-use configurations. However, urban planning is a complex process. Existing studies usually ignore the need of personalized human guidance in planning, and spatial hierarchical structure in planning generation. Moreover, the lack of large-scale land-use configuration samples poses a data sparsity challenge. This paper studies a novel deep human guided urban planning method to jointly solve the above challenges. Specifically, we formulate the problem into a deep conditional variational autoencoder based framework. In this framework, we exploit the deep encoder-decoder design to generate land-use configurations. To capture the spatial hierarchy structure of land uses, we enforce the decoder to generate both the coarse-grained layer of functional zones, and the fine-grained layer of POI distributions. To integrate human guidance, we allow humans to describe what they need as texts and use these texts as a model condition input. To mitigate training data sparsity and improve model robustness, we introduce a variational Gaussian embedding mechanism. It not just allows us to better approximate the embedding space distribution of training data and sample a larger population to overcome sparsity, but also adds more probabilistic randomness into the urban planning generation to improve embedding diversity so as to improve robustness. Finally, we present extensive experiments to validate the enhanced performances of our method.