# Iterative Deep Learning for Road Topology Extraction

arXiv: https://arxiv.org/abs/1808.09814 · HTML: https://mlsanity.com/paper/1808.09814/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2018.

## Built on by (most cited first, 9 of 9)

- [Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images](https://mlsanity.com/paper/2110.01997/) (2021)
- [Topology Preserving Local Road Network Estimation from Single Onboard Camera Image](https://mlsanity.com/paper/2112.10155/) (2021)
- [Improving Online Lane Graph Extraction by Object-Lane Clustering](https://mlsanity.com/paper/2307.10947/) (2023)
- [Road Extraction from Overhead Images with Graph Neural Networks](https://mlsanity.com/paper/2112.05215/) (2021)
- [Intelligent Traffic Monitoring with Hybrid AI](https://mlsanity.com/paper/2209.00448/) (2022)
- [Prior Based Online Lane Graph Extraction from Single Onboard Camera Image](https://mlsanity.com/paper/2307.13344/) (2023)
- [Online Lane Graph Extraction from Onboard Video](https://mlsanity.com/paper/2304.00930/) (2023)
- [Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation](https://mlsanity.com/paper/2312.04744/) (2023)
- [Beyond Endpoints: Path-Centric Reasoning for Vectorized Off-Road Network Extraction](https://mlsanity.com/paper/2512.10416/) (2025)

## Abstract (arXiv)

This paper tackles the task of estimating the topology of road networks from aerial images. Building on top of a global model that performs a dense semantical classification of the pixels of the image, we design a Convolutional Neural Network (CNN) that predicts the local connectivity among the central pixel of an input patch and its border points. By iterating this local connectivity we sweep the whole image and infer the global topology of the road network, inspired by a human delineating a complex network with the tip of their finger. We perform an extensive and comprehensive qualitative and quantitative evaluation on the road network estimation task, and show that our method also generalizes well when moving to networks of retinal vessels.