Abstract
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.
Abstract (translated)
地图是视觉化并代表现实世界的基本媒介。第三波信息的涌现使得大量地图可以随时生成，这将极大地丰富我们理解现实世界特征的维度和角度。然而，大多数地图数据集从未被发现、获取和使用，许多应用程序使用的地图数据可能不完全符合这些应用程序的真实需求。这个挑战是因为缺乏大量为实施深度学习方法进行标注的 benchmark 数据集。因此，我们开发了一个大规模基准数据集，包括用于地图文本注释识别、地图场景分类、地图超分辨率重建和地图风格转移的 well-labelled 数据集。此外，这些 well-labelled 数据集将促进最先进的机器智能技术进行地图特征检测、地图模式识别和地图内容检索。我们希望我们的努力能够为 AI 增强的地理应用提供帮助。
URL
https://arxiv.org/abs/2312.08600
