高分辨率卫星图数据集，助力自动驾驶地图构建
OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction
- 提供像素级实例标注的高清卫星图像
- 覆盖20级分辨率，规模为同类最大
- 适配nuScenes和Argoverse 2，支持自动驾驶研究
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。地图构建是导航与自动驾驶等交通领域的重要基础，从卫星图像中提取道路结构是高效构建地图的关键方法。然而，现有卫星数据集仅提供粗粒度语义标签且分辨率较低（最高至20级），限制了该领域的发展。相比之下，OpenSatMap具备四大优势：（1）细粒度实例级标注；（2）图像分辨率高达20级；（3）目前同类中规模最大；（4）数据具有高度多样性。此外，OpenSatMap与流行的nuScenes和Argoverse 2数据集在地理上对齐，有望推动自动驾驶技术发展。通过发布与维护该数据集，我们为基于卫星的地图构建及下游任务（如自动驾驶）提供了高质量基准。
原文摘要 · Abstract (English)
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.
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