# NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Non-Local Operations

arXiv: https://arxiv.org/abs/1908.08223 · HTML: https://mlsanity.com/paper/1908.08223/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [From Ideal to Real: Unified and Data-Efficient Dense Prediction for Real-World Scenarios](https://mlsanity.com/paper/2506.20279/) (2025)
- [Large-scale Weakly Supervised Learning for Road Extraction from Satellite Imagery](https://mlsanity.com/paper/2309.07823/) (2023)
- [Laplacian Frequency Interaction Network for Rural Thematic Road Extraction](https://mlsanity.com/paper/2605.02866/) (2026)

## Abstract (arXiv)

Road extraction from very high resolution satellite (VHR) images is one of the most important topics in the field of remote sensing. In this paper, we propose an efficient Non-Local LinkNet with non-local blocks that can grasp relations between global features. This enables each spatial feature point to refer to all other contextual information and results in more accurate road segmentation. In detail, our single model without any post-processing like CRF refinement, performed better than any other published state-of-the-art ensemble model in the official DeepGlobe Challenge. Moreover, our NL-LinkNet beat the D-LinkNet, the winner of the DeepGlobe challenge, with 43 % less parameters, less giga floating-point operations per seconds (GFLOPs) and shorter training convergence time. We also present empirical analyses on the proper usages of non-local blocks for the baseline model.