  Maps are an important medium that enable people to comprehensively understand
the configuration of cultural activities and natural elements over different
times and places. Although massive maps are available in the digital era, how
to effectively and accurately access the required map remains a challenge
today. Previous works partially related to map-type classification mainly
focused on map comparison and map matching at the local scale. The features
derived from local map areas might be insufficient to characterize map content.
To facilitate establishing an automatic approach for accessing the needed map,
this paper reports our investigation into using deep learning techniques to
recognize seven types of map, including topographic map, terrain map, physical
map, urban scene map, the National Map, 3D map, nighttime map, orthophoto map,
and land cover classification map. Experimental results show that the
state-of-the-art deep convolutional neural networks can support automatic
map-type classification. Additionally, the classification accuracy varies
according to different map-types. We hope our work can contribute to the
implementation of deep learning techniques in cartographical community and
advance the progress of Geographical Artificial Intelligence (GeoAI).
