# GlobalMapper: Arbitrary-Shaped Urban Layout Generation

arXiv: https://arxiv.org/abs/2307.09693 · HTML: https://mlsanity.com/paper/2307.09693/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 76 references that are in the map.
- First posted on arXiv 19 Jul 2023.

## Builds on (most cited first, 27 of 27)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Interpreting the Latent Space of GANs for Semantic Face Editing](https://mlsanity.com/paper/1907.10786/) (2019)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [LayoutDM: Discrete Diffusion Model for Controllable Layout Generation](https://mlsanity.com/paper/2303.08137/) (2023)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [LayoutTransformer: Layout Generation and Completion with Self-attention](https://mlsanity.com/paper/2006.14615/) (2020)
- [LayoutVAE: Stochastic Scene Layout Generation From a Label Set](https://mlsanity.com/paper/1907.10719/) (2019)
- [Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models](https://mlsanity.com/paper/1811.12463/) (2018)
- [Variational Transformer Networks for Layout Generation](https://mlsanity.com/paper/2104.02416/) (2021)
- [House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation](https://mlsanity.com/paper/2003.06988/) (2020)
- [Constrained Graphic Layout Generation via Latent Optimization](https://mlsanity.com/paper/2108.00871/) (2021)
- [READ: Recursive Autoencoders for Document Layout Generation](https://mlsanity.com/paper/1909.00302/) (2019)
- [Generative Layout Modeling using Constraint Graphs](https://mlsanity.com/paper/2011.13417/) (2020)
- [PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images](https://mlsanity.com/paper/2111.15491/) (2021)
- [DARNet: Deep Active Ray Network for Building Segmentation](https://mlsanity.com/paper/1905.05889/) (2019)
- [SSN: Soft Shadow Network for Image Compositing](https://mlsanity.com/paper/2007.08211/) (2020)
- [Controllable Shadow Generation Using Pixel Height Maps](https://mlsanity.com/paper/2207.05385/) (2022)
- [Diffusion-based Document Layout Generation](https://mlsanity.com/paper/2303.10787/) (2023)
- [Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation](https://mlsanity.com/paper/2104.13316/) (2021)
- [PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositing](https://mlsanity.com/paper/2303.00137/) (2023)
- [DeepTree: Modeling Trees with Situated Latents](https://mlsanity.com/paper/2305.05153/) (2023)
- [Design and Deployment of Photo2Building: A Cloud-based Procedural Modeling Tool as a Service](https://mlsanity.com/paper/2008.01286/) (2020)
- [GLObal Building heights for Urban Studies (UT-GLOBUS) for city- and street- scale urban simulations: Development and first applications](https://mlsanity.com/paper/2205.12224/) (2022)

## Built on by (most cited first, 15 of 15)

- [Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization](https://mlsanity.com/paper/2410.03190/) (2024)
- [CityGen: Infinite and Controllable City Layout Generation](https://mlsanity.com/paper/2312.01508/) (2023)
- [FINEMATCH: Aspect-based Fine-grained Image and Text Mismatch Detection and Correction](https://mlsanity.com/paper/2404.14715/) (2024)
- [Kubrick: Multimodal Agent Collaborations for Synthetic Video Generation](https://mlsanity.com/paper/2408.10453/) (2024)
- [COHO: Context-Sensitive City-Scale Hierarchical Urban Layout Generation](https://mlsanity.com/paper/2407.11294/) (2024)
- [MajutsuCity: Language-driven Aesthetic-adaptive City Generation with Controllable 3D Assets and Layouts](https://mlsanity.com/paper/2511.20415/) (2025)
- [Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignment](https://mlsanity.com/paper/2412.00306/) (2024)
- [GroundUp: Rapid Sketch-Based 3D City Massing](https://mlsanity.com/paper/2407.12739/) (2024)
- [From Geometric Mimicry to Comprehensive Generation: A Context-Informed Multimodal Diffusion Model for Urban Morphology Synthesis](https://mlsanity.com/paper/2409.17049/) (2024)
- [Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation](https://mlsanity.com/paper/2411.03223/) (2024)
- [Computer-Aided Layout Generation for Building Design: A Review](https://mlsanity.com/paper/2504.09694/) (2025)
- [RoBus: A Multimodal Dataset for Controllable Road Networks and Building Layouts Generation](https://mlsanity.com/paper/2407.07835/) (2024)
- [Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models](https://mlsanity.com/paper/2503.18337/) (2025)
- [Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation](https://mlsanity.com/paper/2507.08513/) (2025)
- [CoMa: Contextual Massing Generation with Vision-Language Models](https://mlsanity.com/paper/2601.08464/) (2026)

## Abstract (arXiv)

Modeling and designing urban building layouts is of significant interest in computer vision, computer graphics, and urban applications. A building layout consists of a set of buildings in city blocks defined by a network of roads. We observe that building layouts are discrete structures, consisting of multiple rows of buildings of various shapes, and are amenable to skeletonization for mapping arbitrary city block shapes to a canonical form. Hence, we propose a fully automatic approach to building layout generation using graph attention networks. Our method generates realistic urban layouts given arbitrary road networks, and enables conditional generation based on learned priors. Our results, including user study, demonstrate superior performance as compared to prior layout generation networks, support arbitrary city block and varying building shapes as demonstrated by generating layouts for 28 large cities.