Recently, road graph extraction has garnered increasing attention due to its crucial role in autonomous driving, navigation, etc. However, accurately and efficiently extracting road graphs remains a persistent challenge, primarily due to the severe scarcity of labeled data. To address this limitation, we collect a global-scale satellite road graph extraction dataset, i.e. Global-Scale dataset. Specifically, the Global-Scale dataset is ∼20× larger than the largest existing public road extraction dataset and spans over 13,800 km2 globally. Additionally, we develop a novel road graph extraction model, i.e. SAM-Road++, which adopts a node-guided resampling method to alleviate the mismatch issue between training and inference in SAM-Road, a pioneering state-of-the-art road graph extraction model. Furthermore, we propose a simple yet effective ``extended-line'' strategy in SAM-Road++ to mitigate the occlusion issue on the road. Extensive experiments demonstrate the validity of the collected Global-Scale dataset and the proposed SAM-Road++ method, particularly highlighting its superior predictive power in unseen regions. The dataset and code are available at \url{https://github.com/earth-insights/samroadplus}.
@article{arxiv.2411.16733,
title = {Towards Satellite Image Road Graph Extraction: A Global-Scale Dataset and A Novel Method},
author = {Pan Yin and Kaiyu Li and Xiangyong Cao and Jing Yao and Lei Liu and Xueru Bai and Feng Zhou and Deyu Meng},
journal= {arXiv preprint arXiv:2411.16733},
year = {2024}
}