{"ID":2879398,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16272","arxiv_id":"2508.16272","title":"IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization","abstract":"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","short_abstract":"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. Howev...","url_abs":"https://arxiv.org/abs/2508.16272","url_pdf":"https://arxiv.org/pdf/2508.16272v1","authors":"[\"Yu Meng\",\"Ligao Deng\",\"Zhihao Xi\",\"Jiansheng Chen\",\"Jingbo Chen\",\"Anzhi Yue\",\"Diyou Liu\",\"Kai Li\",\"Chenhao Wang\",\"Kaiyu Li\",\"Yupeng Deng\",\"Xian Sun\"]","published":"2025-08-22T10:14:18Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":610584,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2879398,"paper_url":"https://arxiv.org/abs/2508.16272","paper_title":"IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization","repo_url":"https://github.com/ucas-dlg/IRSAMap","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}