# Automated Urban Planning aware Spatial Hierarchies and Human Instructions

arXiv: https://arxiv.org/abs/2209.13002 · HTML: https://mlsanity.com/paper/2209.13002/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 26 Sep 2022.

## Builds on (most cited first, 18 of 18)

- [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)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.03242/) (2016)
- [Autoencoding beyond pixels using a learned similarity metric](https://mlsanity.com/paper/1512.09300/) (2015)
- [MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment](https://mlsanity.com/paper/1709.06298/) (2017)
- [An Introduction to Deep Generative Modeling](https://mlsanity.com/paper/2103.05180/) (2021)
- [Guided Variational Autoencoder for Disentanglement Learning](https://mlsanity.com/paper/2004.01255/) (2020)
- [Conditional molecular design with deep generative models](https://mlsanity.com/paper/1805.00108/) (2018)
- [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)
- [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)
- [Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation](https://mlsanity.com/paper/2112.14699/) (2021)
- [CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models](https://mlsanity.com/paper/2004.01215/) (2020)

## Built on by (most cited first, 1 of 1)

- [Generative AI Meets Future Cities: Towards an Era of Autonomous Urban Intelligence](https://mlsanity.com/paper/2304.03892/) (2023)

## Abstract (arXiv)

Traditional urban planning demands urban experts to spend considerable time and effort producing an optimal urban plan under many architectural constraints. The remarkable imaginative ability of deep generative learning provides hope for renovating urban planning. While automated urban planners have been examined, they are constrained because of the following: 1) neglecting human requirements in urban planning; 2) omitting spatial hierarchies in urban planning, and 3) lacking numerous urban plan data samples. To overcome these limitations, we propose a novel, deep, human-instructed urban planner. In the preliminary work, we formulate it into an encoder-decoder paradigm. The encoder is to learn the information distribution of surrounding contexts, human instructions, and land-use configuration. The decoder is to reconstruct the land-use configuration and the associated urban functional zones. The reconstruction procedure will capture the spatial hierarchies between functional zones and spatial grids. Meanwhile, we introduce a variational Gaussian mechanism to mitigate the data sparsity issue. Even though early work has led to good results, the performance of generation is still unstable because the way spatial hierarchies are captured may lead to unclear optimization directions. In this journal version, we propose a cascading deep generative framework based on generative adversarial networks (GANs) to solve this problem, inspired by the workflow of urban experts. In particular, the purpose of the first GAN is to build urban functional zones based on information from human instructions and surrounding contexts. The second GAN will produce the land-use configuration based on the functional zones that have been constructed. Additionally, we provide a conditioning augmentation module to augment data samples. Finally, we conduct extensive experiments to validate the efficacy of our work.