Towards Satellite Image Road Graph Extraction: A Global-Scale Dataset and A Novel Method

• URL: http://arxiv.org/abs/2411.16733v1
• Date: Sat, 23 Nov 2024 10:26:07 GMT
• Title: Towards Satellite Image Road Graph Extraction: A Global-Scale Dataset and A Novel Method
• Authors: Pan Yin, Kaiyu Li, Xiangyong Cao, Jing Yao, Lei Liu, Xueru Bai, Feng Zhou, Deyu Meng,
• Abstract summary: We collect a global-scale satellite road graph extraction dataset, i.e. Global-Scale dataset. We develop a novel road graph extraction model, i.e. SAM-Road++, which adopts a node-guided resampling method. Experiments demonstrate the validity of the collected Global-Scale dataset and the proposed SAM-Road++ method.
• Score: 42.3609654615897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\sim20 \times$ larger than the largest existing public road extraction dataset and spans over 13,800 $km^2$ 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}.

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