Deep convolutional neural networks for map-type classification

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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 a massive number of maps are available in the digital era, how to effectively and accurately locate and access the desired map on the Internet 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, terrain, physical, urban scene, the National Map, 3D, nighttime, orthophoto, and land cover classification. 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. This work can contribute to the implementation of deep learning techniques in the cartographic community and advance the progress of Geographical Artificial Intelligence (GeoAI).
Publication type Conference Paper
Publication Subtype Conference Paper
Title Deep convolutional neural networks for map-type classification
Year Published 2019
Language English
Publisher Cartography and Geographic Information Society (CaGIS)
Contributing office(s) Center for Geospatial Information Science (CEGIS)
Description 6 p.
Larger Work Type Book
Larger Work Subtype Conference publication
Larger Work Title Autocarto 2018: Proceedings
Conference Title AutoCarto 2018, the 22nd International Research Symposium on Computer-based Cartography
Conference Location Madison, WI
Conference Date May 22-24, 2018
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