mlsanityData to 14 Oct 2026

GlobalMapper: Arbitrary-Shaped Urban Layout Generation

arXiv 2307.09693 ↗First posted 19 Jul 2023cs.CVcited by 15

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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.

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

27 of 76 references.

References · 27

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2019Interpreting the Latent Space of GANs for Semantic Face Editingcited by 695
  5. 2020A Generalization of Transformer Networks to Graphscited by 661
  6. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 506
  7. 2018DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Imagescited by 277
  8. 2023LayoutDM: Discrete Diffusion Model for Controllable Layout Generationcited by 143
  9. 2019LayoutGAN: Generating Graphic Layouts with Wireframe Discriminatorscited by 131
  10. 2020LayoutTransformer: Layout Generation and Completion with Self-attentioncited by 123
  11. 2019LayoutVAE: Stochastic Scene Layout Generation From a Label Setcited by 119
  12. 2018Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Modelscited by 106
15 more
  1. 2021Variational Transformer Networks for Layout Generationcited by 88
  2. 2020House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generationcited by 87
  3. 2021Constrained Graphic Layout Generation via Latent Optimizationcited by 83
  4. 2019READ: Recursive Autoencoders for Document Layout Generationcited by 51
  5. 2020Generative Layout Modeling using Constraint Graphscited by 46
  6. 2021PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Imagescited by 43
  7. 2019DARNet: Deep Active Ray Network for Building Segmentationcited by 30
  8. 2020SSN: Soft Shadow Network for Image Compositingcited by 26
  9. 2022Controllable Shadow Generation Using Pixel Height Mapscited by 22
  10. 2023Diffusion-based Document Layout Generationcited by 21
  11. 2021Building-GAN: Graph-Conditioned Architectural Volumetric Design Generationcited by 20
  12. 2023PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositingcited by 15
  13. 2023DeepTree: Modeling Trees with Situated Latentscited by 9
  14. 2020Design and Deployment of Photo2Building: A Cloud-based Procedural Modeling Tool as a Servicecited by 6
  15. 2022GLObal Building heights for Urban Studies (UT-GLOBUS) for city- and street- scale urban simulations: Development and first applicationscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 9; 2025: 4; 2026: 1.

15 papers.

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  1. 2024Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimizationcited by 26
  2. 2023CityGen: Infinite and Controllable City Layout Generationcited by 22
  3. 2024FINEMATCH: Aspect-based Fine-grained Image and Text Mismatch Detection and Correctioncited by 17
  4. 2024Kubrick: Multimodal Agent Collaborations for Synthetic Video Generationcited by 16
  5. 2024COHO: Context-Sensitive City-Scale Hierarchical Urban Layout Generationcited by 10
  6. 2025MajutsuCity: Language-driven Aesthetic-adaptive City Generation with Controllable 3D Assets and Layoutscited by 6
  7. 2024Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignmentcited by 5
  8. 2024GroundUp: Rapid Sketch-Based 3D City Massingcited by 4
  9. 2024From Geometric Mimicry to Comprehensive Generation: A Context-Informed Multimodal Diffusion Model for Urban Morphology Synthesiscited by 3
  10. 2024Beyond Grid Data: Exploring Graph Neural Networks for Earth Observationcited by 3
  11. 2025Computer-Aided Layout Generation for Building Design: A Reviewcited by 3
  12. 2024RoBus: A Multimodal Dataset for Controllable Road Networks and Building Layouts Generationcited by 2
3 more
  1. 2025Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Modelscited by 2
  2. 2025Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generationcited by 0
  3. 2026CoMa: Contextual Massing Generation with Vision-Language Modelscited by 0