# Scribble-based Weakly Supervised Deep Learning for Road Surface Extraction from Remote Sensing Images

arXiv: https://arxiv.org/abs/2010.13106 · HTML: https://mlsanity.com/paper/2010.13106/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [Geographical Knowledge-driven Representation Learning for Remote Sensing Images](https://mlsanity.com/paper/2107.05276/) (2021)
- [Scribble-Supervised RGB-T Salient Object Detection](https://mlsanity.com/paper/2303.09733/) (2023)
- [Scribble-based Boundary-aware Network for Weakly Supervised Salient Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2202.03501/) (2022)
- [Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2204.09120/) (2022)
- [A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images-Analysis Unit,Model Scalability and Transferability](https://mlsanity.com/paper/2202.03342/) (2022)
- [Scribbles for All: Benchmarking Scribble Supervised Segmentation Across Datasets](https://mlsanity.com/paper/2408.12489/) (2024)
- [Land Cover Mapping in Limited Labels Scenario: A Survey](https://mlsanity.com/paper/2103.02429/) (2021)
- [SA-MixNet: Structure-aware Mixup and Invariance Learning for Scribble-supervised Road Extraction in Remote Sensing Images](https://mlsanity.com/paper/2403.01381/) (2024)
- [Habitat classification from satellite observations with sparse annotations](https://mlsanity.com/paper/2209.12995/) (2022)
- [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)
- [Self-Supervised Masked Digital Elevation Models Encoding for Low-Resource Downstream Tasks](https://mlsanity.com/paper/2309.03367/) (2023)
- [Learning Semantic Segmentation with Query Points Supervision on Aerial Images](https://mlsanity.com/paper/2309.05490/) (2023)
- [Skeleton Supervised Airway Segmentation](https://mlsanity.com/paper/2403.06510/) (2024)
- [From Pixels to Images: A Structural Survey of Deep Learning Paradigms in Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2505.15147/) (2025)
- [DeH4R: A Decoupled and Hybrid Method for Road Network Graph Extraction](https://mlsanity.com/paper/2508.13669/) (2025)

## Abstract (arXiv)

Road surface extraction from remote sensing images using deep learning methods has achieved good performance, while most of the existing methods are based on fully supervised learning, which requires a large amount of training data with laborious per-pixel annotation. In this paper, we propose a scribble-based weakly supervised road surface extraction method named ScRoadExtractor, which learns from easily accessible scribbles such as centerlines instead of densely annotated road surface ground-truths. To propagate semantic information from sparse scribbles to unlabeled pixels, we introduce a road label propagation algorithm which considers both the buffer-based properties of road networks and the color and spatial information of super-pixels. The proposal masks generated from the road label propagation algorithm are utilized to train a dual-branch encoder-decoder network we designed, which consists of a semantic segmentation branch and an auxiliary boundary detection branch. We perform experiments on three diverse road datasets that are comprised of highresolution remote sensing satellite and aerial images across the world. The results demonstrate that ScRoadExtractor exceed the classic scribble-supervised segmentation method by 20% for the intersection over union (IoU) indicator and outperform the state-of-the-art scribble-based weakly supervised methods at least 4%.