CartoMark: a benchmark dataset for map pattern recognition and 1 map
  content retrieval with machine intelligence
Xiran Zhou Yi Wen Honghao Li Kaiyuan Li Zhenfeng Shao Zhigang Yan Xiao Xie
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.