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

With the continuous enhancement of remote sensing image resolution and the rapid advancement of deep learning techniques, land cover mapping is undergoing a significant transformation from pixel-level segmentation to object-based vector modeling. This shift imposes higher demands on deep learning models, requiring not only precise delineation of object boundaries but also the preservation of topological consistency among geographic elements. However, existing public datasets face three major limitations: limited class annotations, restricted data scale, and the lack of spatial structural information, which severely hinder the development of breakthrough methods in high-resolution remote sensing vectorization. To address these challenges, we present IRSAMap, the first global remote sensing dataset designed for large-scale, high-resolution, multifeature land cover vector mapping. This dataset offers four key advantages. First, a comprehensive element vector annotation system that includes over 1.8 million instances of ten typical natural and man-made objects, such as buildings, roads, rivers, and trees, using a unified vector annotation standard framework that ensures both semantic integrity and spatial structural accuracy. Second, an intelligent annotation workflow incorporating “manual preannotation + AI-based training and inference + manual review and correction,” which enhances annotation efficiency while ensuring consistency. Third, a global coverage that spans 79 regions across six continents, representing diverse terrain types, including urban and rural areas, with a total coverage area exceeding 1000 km2. Fourth, multitask adaptability, supporting various tasks such as pixel-level land cover classification, building outline regularization extraction, road centerline extraction, and panoramic segmentation. As a fundamental resource for remote sensing intelligent interpretation, IRSAMap provides a standardized benchmark for the paradigm shift from pixels to objects, which will significantly advance the development of high-precision geographic feature automation, collaborative modeling, and other cutting-edge research directions. The dataset is of great value for applications such as global geographic information updating and digital twin construction. IRSAMap is publicly available at https://github.com/ucas-dlg/IRSAMap