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Deep convolutional neural networks for map-type classification

December 31, 2018

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).

Citation Information

Publication Year 2019
Title Deep convolutional neural networks for map-type classification
Authors Xiran Zhou, Wenwen Li, Samantha Arundel, Jun Liu
Publication Type Conference Paper
Publication Subtype Conference Paper
Index ID 70206267
Record Source USGS Publications Warehouse
USGS Organization Center for Geospatial Information Science (CEGIS)