IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization

• URL: http://arxiv.org/abs/2508.16272v1
• Date: Fri, 22 Aug 2025 10:14:18 GMT
• Title: IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization
• 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,
• Abstract summary: IRSAMap is the first global remote sensing dataset for large-scale, high-resolution, multi-feature land cover vector mapping.<n>IRSAMap offers four key advantages: 1) a comprehensive vector annotation system with over 1.8 million instances of 10 typical objects, 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.
• Score: 37.95801085939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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