Maps are fundamental medium to visualize and represent the real word in a simple and 16 philosophical way. The emergence of the 3rd wave information has made a proportion of maps are available to be generated ubiquitously, which would significantly enrich the dimensions and perspectives to understand the characteristics of the real world. However, a majority of map dataset have never been discovered, acquired and effectively used, and the map data used in many applications might not be completely fitted for the authentic demands of these applications. This challenge is emerged due to the lack of numerous well-labelled benchmark datasets for implementing the deep learning approaches into identifying complicated map content. Thus, we develop a large-scale benchmark dataset that includes well-labelled dataset for map text annotation recognition, map scene classification, map super-resolution reconstruction, and map style transferring. Furthermore, these well-labelled datasets would facilitate the state-of-the-art machine intelligence technologies to conduct map feature detection, map pattern recognition and map content retrieval. We hope our efforts would be useful for AI-enhanced cartographical applications.

翻译：地图是以简洁而富有哲理的方式可视化并表达现实世界的基本媒介。第三次信息浪潮的涌现使得部分地图能够被普遍生成，这将极大丰富理解现实世界特征的多维视角。然而，绝大多数地图数据集从未被发掘、获取并有效利用，许多应用中使用的地图数据可能并不完全契合其真实需求。这一挑战源于缺乏大量精心标注的基准数据集来实施深度学习方法以识别复杂地图内容。为此，我们构建了一个大规模基准数据集，其中包含用于地图文本标注识别、地图场景分类、地图超分辨率重建及地图风格转换的精心标注数据集。这些标注完善的数据集将进一步推动最先进的机器智能技术进行地图特征检测、地图模式识别与地图内容检索。我们期望这些工作能为人工智能增强的制图应用提供助力。