# Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery

arXiv: https://arxiv.org/abs/1806.05182 · HTML: https://mlsanity.com/paper/1806.05182/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 11 of 11)

- [Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction](https://mlsanity.com/paper/2303.08815/) (2023)
- [Segment anything, from space?](https://mlsanity.com/paper/2304.13000/) (2023)
- [Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction](https://mlsanity.com/paper/2111.15119/) (2021)
- [Fully convolutional Siamese neural networks for buildings damage assessment from satellite images](https://mlsanity.com/paper/2111.00508/) (2021)
- [LDPoly: Latent Diffusion for Polygonal Road Outline Extraction in Large-Scale Topographic Mapping](https://mlsanity.com/paper/2504.20645/) (2025)
- [PaRK-Detect: Towards Efficient Multi-Task Satellite Imagery Road Extraction via Patch-Wise Keypoints Detection](https://mlsanity.com/paper/2302.13263/) (2023)
- [Flood Mapping from RGB imagery using a Vision Foundation Model](https://mlsanity.com/paper/2606.24120/) (2026)
- [CrosswalkNet: An Optimized Deep Learning Framework for Pedestrian Crosswalk Detection in Aerial Images with High-Performance Computing](https://mlsanity.com/paper/2506.07885/) (2025)
- [Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning](https://mlsanity.com/paper/2209.14364/) (2022)
- [Large-scale Weakly Supervised Learning for Road Extraction from Satellite Imagery](https://mlsanity.com/paper/2309.07823/) (2023)
- [Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation](https://mlsanity.com/paper/2312.04744/) (2023)

## Abstract (arXiv)

Analysis of high-resolution satellite images has been an important research topic for traffic management, city planning, and road monitoring. One of the problems here is automatic and precise road extraction. From an original image, it is difficult and computationally expensive to extract roads due to presences of other road-like features with straight edges. In this paper, we propose an approach for automatic road extraction based on a fully convolutional neural network of U-net family. This network consists of ResNet-34 pre-trained on ImageNet and decoder adapted from vanilla U-Net. Based on validation results, leaderboard and our own experience this network shows superior results for the DEEPGLOBE - CVPR 2018 road extraction sub-challenge. Moreover, this network uses moderate memory that allows using just one GTX 1080 or 1080ti video cards to perform whole training and makes pretty fast predictions.