# RNGDet: Road Network Graph Detection by Transformer in Aerial Images

arXiv: https://arxiv.org/abs/2202.07824 · HTML: https://mlsanity.com/paper/2202.07824/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 53 references that are in the map.
- First posted on arXiv 16 Feb 2022.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [Sequence Level Training with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06732/) (2015)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Attention Augmented Convolutional Networks](https://mlsanity.com/paper/1904.09925/) (2019)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [RoadTracer: Automatic Extraction of Road Networks from Aerial Images](https://mlsanity.com/paper/1802.03680/) (2018)
- [Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images](https://mlsanity.com/paper/2110.01997/) (2021)
- [Line Segment Detection Using Transformers without Edges](https://mlsanity.com/paper/2101.01909/) (2021)
- [Topological Map Extraction from Overhead Images](https://mlsanity.com/paper/1812.01497/) (2018)
- [Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding](https://mlsanity.com/paper/2007.09547/) (2020)
- [DAGMapper: Learning to Map by Discovering Lane Topology](https://mlsanity.com/paper/2012.12377/) (2020)
- [Hierarchical Recurrent Attention Networks for Structured Online Maps](https://mlsanity.com/paper/2012.12314/) (2020)
- [Convolutional Recurrent Network for Road Boundary Extraction](https://mlsanity.com/paper/2012.12160/) (2020)
- [iCurb: Imitation Learning-based Detection of Road Curbs using Aerial Images for Autonomous Driving](https://mlsanity.com/paper/2103.17118/) (2021)
- [Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving](https://mlsanity.com/paper/2103.17119/) (2021)
- [SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving](https://mlsanity.com/paper/2109.07701/) (2021)
- [Image-Conditioned Graph Generation for Road Network Extraction](https://mlsanity.com/paper/1910.14388/) (2019)
- [Automatic Construction of Lane-level HD Maps for Urban Scenes](https://mlsanity.com/paper/2107.10972/) (2021)
- [City-Scale Road Extraction from Satellite Imagery v2: Road Speeds and Travel Times](https://mlsanity.com/paper/1908.09715/) (2019)
- [csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps](https://mlsanity.com/paper/2111.06020/) (2021)
- [GeoGAN: A Conditional GAN with Reconstruction and Style Loss to Generate Standard Layer of Maps from Satellite Images](https://mlsanity.com/paper/1902.05611/) (2019)
- [Transferring Multiscale Map Styles Using Generative Adversarial Networks](https://mlsanity.com/paper/1905.02200/) (2019)

## Built on by (most cited first, 26 of 26)

- [Segment Anything Model for Road Network Graph Extraction](https://mlsanity.com/paper/2403.16051/) (2024)
- [RNGDet++: Road Network Graph Detection by Transformer with Instance Segmentation and Multi-scale Features Enhancement](https://mlsanity.com/paper/2209.10150/) (2022)
- [Learning and Aggregating Lane Graphs for Urban Automated Driving](https://mlsanity.com/paper/2302.06175/) (2023)
- [InsMapper: Exploring Inner-instance Information for Vectorized HD Mapping](https://mlsanity.com/paper/2308.08543/) (2023)
- [CenterLineDet: CenterLine Graph Detection for Road Lanes with Vehicle-mounted Sensors by Transformer for HD Map Generation](https://mlsanity.com/paper/2209.07734/) (2022)
- [Building Lane-Level Maps from Aerial Images](https://mlsanity.com/paper/2312.13449/) (2023)
- [Towards Satellite Image Road Graph Extraction: A Global-Scale Dataset and A Novel Method](https://mlsanity.com/paper/2411.16733/) (2024)
- [Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks](https://mlsanity.com/paper/2404.06860/) (2024)
- [Patched Line Segment Learning for Vector Road Mapping](https://mlsanity.com/paper/2309.02923/) (2023)
- [IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization](https://mlsanity.com/paper/2508.16272/) (2025)
- [HoliTracer: Holistic Vectorization of Geographic Objects from Large-Size Remote Sensing Imagery](https://mlsanity.com/paper/2507.16251/) (2025)
- [LDPoly: Latent Diffusion for Polygonal Road Outline Extraction in Large-Scale Topographic Mapping](https://mlsanity.com/paper/2504.20645/) (2025)
- [GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs](https://mlsanity.com/paper/2502.11731/) (2025)
- [GLD-Road:A global-local decoding road network extraction model for remote sensing images](https://mlsanity.com/paper/2506.09553/) (2025)
- [DIGMAPPER: A Modular System for Automated Geologic Map Digitization](https://mlsanity.com/paper/2506.16006/) (2025)
- [Mastering Spatial Graph Prediction of Road Networks](https://mlsanity.com/paper/2210.00828/) (2022)
- [Leveraging GNSS and Onboard Visual Data from Consumer Vehicles for Robust Road Network Estimation](https://mlsanity.com/paper/2408.01640/) (2024)
- [InVDriver: Intra-Instance Aware Vectorized Query-Based Autonomous Driving Transformer](https://mlsanity.com/paper/2502.17949/) (2025)
- [UniVector: Unified Vector Extraction via Instance-Geometry Interaction](https://mlsanity.com/paper/2510.13234/) (2025)
- [Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps](https://mlsanity.com/paper/2304.14972/) (2023)
- [Guided Scale Space Radon Transform for linear structures detection](https://mlsanity.com/paper/2311.09103/) (2023)
- [DeH4R: A Decoupled and Hybrid Method for Road Network Graph Extraction](https://mlsanity.com/paper/2508.13669/) (2025)
- [DOGE: Differentiable Bezier Graph Optimization for Road Network Extraction](https://mlsanity.com/paper/2511.19850/) (2025)
- [LineGraph2Road: Structural Graph Reasoning on Line Graphs for Road Network Extraction](https://mlsanity.com/paper/2602.23290/) (2026)
- [MapDreamer: Aerial Imagery Conditioned Latent Diffusion for Lane-Level Map Generation](https://mlsanity.com/paper/2607.01370/) (2026)
- [TraversRL: Traversable Pedestrian Pathway Generation With Reinforcement Learning](https://mlsanity.com/paper/2607.17479/) (2026)

## Abstract (arXiv)

Road network graphs provide critical information for autonomous-vehicle applications, such as drivable areas that can be used for motion planning algorithms. To find road network graphs, manually annotation is usually inefficient and labor-intensive. Automatically detecting road network graphs could alleviate this issue, but existing works still have some limitations. For example, segmentation-based approaches could not ensure satisfactory topology correctness, and graph-based approaches could not present precise enough detection results. To provide a solution to these problems, we propose a novel approach based on transformer and imitation learning in this paper. In view of that high-resolution aerial images could be easily accessed all over the world nowadays, we make use of aerial images in our approach. Taken as input an aerial image, our approach iteratively generates road network graphs vertex-by-vertex. Our approach can handle complicated intersection points with various numbers of incident road segments. We evaluate our approach on a publicly available dataset. The superiority of our approach is demonstrated through the comparative experiments. Our work is accompanied with a demonstration video which is available at urlhttps://tonyxuqaq.github.io/projects/RNGDet/.