Towards Satellite Image Road Graph Extraction: A Global-Scale Dataset and A Novel Method
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913585467228160 |
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| author | Yin, Pan Li, Kaiyu Cao, Xiangyong Yao, Jing Liu, Lei Bai, Xueru Zhou, Feng Meng, Deyu |
| author_facet | Yin, Pan Li, Kaiyu Cao, Xiangyong Yao, Jing Liu, Lei Bai, Xueru Zhou, Feng Meng, Deyu |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16733 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Satellite Image Road Graph Extraction: A Global-Scale Dataset and A Novel Method Yin, Pan Li, Kaiyu Cao, Xiangyong Yao, Jing Liu, Lei Bai, Xueru Zhou, Feng Meng, Deyu Computer Vision and Pattern Recognition 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}. |
| title | Towards Satellite Image Road Graph Extraction: A Global-Scale Dataset and A Novel Method |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.16733 |