IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization
Yu Meng, Ligao Deng, Zhihao Xi, Jiansheng Chen, Jingbo Chen, Anzhi Yue, Diyou Liu, Kai Li, Chenhao Wang, Kaiyu Li, Yupeng Deng, and Xian Sun

TL;DR
IRSAMap introduces a comprehensive, large-scale, high-resolution remote sensing dataset with detailed vector annotations across multiple regions, enabling advanced object-based land cover mapping and supporting diverse geographic information tasks.
Contribution
This paper presents IRSAMap, the first global dataset for high-resolution land cover vector mapping, addressing data limitations and supporting multi-task geographic feature extraction.
Findings
Over 1.8 million object instances annotated
Global coverage across 79 regions on six continents
Supports multiple land cover mapping tasks
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…
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