CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence
This addresses the problem of limited map data for AI-enhanced cartographical applications, though it is incremental as it provides a new dataset rather than a novel method.
The authors tackled the lack of well-labeled benchmark datasets for applying deep learning to map content analysis by developing CartoMark, a large-scale dataset for tasks like map text annotation recognition, scene classification, super-resolution reconstruction, and style transfer, which facilitates state-of-the-art machine intelligence technologies for map feature detection, pattern recognition, and content retrieval.
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.