In this paper, we propose OpenSatMap, a fine-grained, high-resolution satellite dataset for large-scale map construction. Map construction is one of the foundations of the transportation industry, such as navigation and autonomous driving. Extracting road structures from satellite images is an efficient way to construct large-scale maps. However, existing satellite datasets provide only coarse semantic-level labels with a relatively low resolution (up to level 19), impeding the advancement of this field. In contrast, the proposed OpenSatMap (1) has fine-grained instance-level annotations; (2) consists of high-resolution images (level 20); (3) is currently the largest one of its kind; (4) collects data with high diversity. Moreover, OpenSatMap covers and aligns with the popular nuScenes dataset and Argoverse 2 dataset to potentially advance autonomous driving technologies. By publishing and maintaining the dataset, we provide a high-quality benchmark for satellite-based map construction and downstream tasks like autonomous driving.

翻译：本文提出OpenSatMap，一个用于大规模地图构建的细粒度高分辨率卫星数据集。地图构建是交通行业（如导航与自动驾驶）的基础之一。从卫星图像中提取道路结构是构建大规模地图的有效途径。然而，现有卫星数据集仅提供相对低分辨率（最高至19级）的粗粒度语义级标签，阻碍了该领域的发展。相比之下，所提出的OpenSatMap具有以下特点：(1) 提供细粒度实例级标注；(2) 包含高分辨率图像（20级）；(3) 是目前同类数据集中规模最大的；(4) 采集的数据具有高度多样性。此外，OpenSatMap覆盖并与主流的nuScenes数据集及Argoverse 2数据集对齐，有望推动自动驾驶技术的发展。通过公开发布并持续维护该数据集，我们为基于卫星的地图构建及自动驾驶等下游任务提供了高质量的基准。