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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.16272 |
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| _version_ | 1866912548969775104 |
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| author | Meng, Yu Deng, Ligao Xi, Zhihao Chen, Jiansheng Chen, Jingbo Yue, Anzhi Liu, Diyou Li, Kai Wang, Chenhao Li, Kaiyu Deng, Yupeng Sun, Xian |
| author_facet | Meng, Yu Deng, Ligao Xi, Zhihao Chen, Jiansheng Chen, Jingbo Yue, Anzhi Liu, Diyou Li, Kai Wang, Chenhao Li, Kaiyu Deng, Yupeng Sun, Xian |
| contents | With the enhancement of remote sensing image resolution and the rapid advancement of deep learning, land cover mapping is transitioning from pixel-level segmentation to object-based vector modeling. This shift demands more from deep learning models, requiring precise object boundaries and topological consistency. However, existing datasets face three main challenges: limited class annotations, small data scale, and lack of spatial structural information. To overcome these issues, we introduce IRSAMap, the first global remote sensing dataset for large-scale, high-resolution, multi-feature land cover vector mapping. IRSAMap offers four key advantages: 1) a comprehensive vector annotation system with over 1.8 million instances of 10 typical objects (e.g., buildings, roads, rivers), ensuring semantic and spatial accuracy; 2) an intelligent annotation workflow combining manual and AI-based methods to improve efficiency and consistency; 3) global coverage across 79 regions in six continents, totaling over 1,000 km; and 4) multi-task adaptability for tasks like pixel-level classification, building outline extraction, road centerline extraction, and panoramic segmentation. IRSAMap provides a standardized benchmark for the shift from pixel-based to object-based approaches, advancing geographic feature automation and collaborative modeling. It is valuable for global geographic information updates and digital twin construction. The dataset is publicly available at https://github.com/ucas-dlg/IRSAMap |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16272 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization Meng, Yu Deng, Ligao Xi, Zhihao Chen, Jiansheng Chen, Jingbo Yue, Anzhi Liu, Diyou Li, Kai Wang, Chenhao Li, Kaiyu Deng, Yupeng Sun, Xian Computer Vision and Pattern Recognition With the enhancement of remote sensing image resolution and the rapid advancement of deep learning, land cover mapping is transitioning from pixel-level segmentation to object-based vector modeling. This shift demands more from deep learning models, requiring precise object boundaries and topological consistency. However, existing datasets face three main challenges: limited class annotations, small data scale, and lack of spatial structural information. To overcome these issues, we introduce IRSAMap, the first global remote sensing dataset for large-scale, high-resolution, multi-feature land cover vector mapping. IRSAMap offers four key advantages: 1) a comprehensive vector annotation system with over 1.8 million instances of 10 typical objects (e.g., buildings, roads, rivers), ensuring semantic and spatial accuracy; 2) an intelligent annotation workflow combining manual and AI-based methods to improve efficiency and consistency; 3) global coverage across 79 regions in six continents, totaling over 1,000 km; and 4) multi-task adaptability for tasks like pixel-level classification, building outline extraction, road centerline extraction, and panoramic segmentation. IRSAMap provides a standardized benchmark for the shift from pixel-based to object-based approaches, advancing geographic feature automation and collaborative modeling. It is valuable for global geographic information updates and digital twin construction. The dataset is publicly available at https://github.com/ucas-dlg/IRSAMap |
| title | IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.16272 |