Item talk:Q314003

{

 "USGS Publications Warehouse": {
   "@context": "https://schema.org",
   "@type": "CreativeWork",
   "additionalType": "Conference Paper",
   "name": "Deep convolutional neural networks for map-type classification",
   "identifier": [
     {
       "@type": "PropertyValue",
       "propertyID": "USGS Publications Warehouse IndexID",
       "value": "70206267",
       "url": "https://pubs.usgs.gov/publication/70206267"
     },
     {
       "@type": "PropertyValue",
       "propertyID": "USGS Publications Warehouse Internal ID",
       "value": 70206267
     }
   ],
   "inLanguage": "en",
   "datePublished": "2019",
   "dateModified": "2020-06-02",
   "abstract": "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).",
   "description": "6 p.",
   "publisher": {
     "@type": "Organization",
     "name": "Cartography and Geographic Information Society (CaGIS)"
   },
   "author": [
     {
       "@type": "Person",
       "name": "Zhou, Xiran",
       "givenName": "Xiran",
       "familyName": "Zhou",
       "affiliation": [
         {
           "@type": "Organization",
           "name": "Arizona State University"
         }
       ]
     },
     {
       "@type": "Person",
       "name": "Li, Wenwen",
       "givenName": "Wenwen",
       "familyName": "Li",
       "identifier": {
         "@type": "PropertyValue",
         "propertyID": "ORCID",
         "value": "0000-0003-2237-9499",
         "url": "https://orcid.org/0000-0003-2237-9499"
       },
       "affiliation": [
         {
           "@type": "Organization",
           "name": "Arizona State University"
         }
       ]
     },
     {
       "@type": "Person",
       "name": "Liu, Jun",
       "givenName": "Jun",
       "familyName": "Liu"
     },
     {
       "@type": "Person",
       "name": "Arundel, Samantha sarundel@usgs.gov",
       "givenName": "Samantha",
       "familyName": "Arundel",
       "email": "sarundel@usgs.gov",
       "identifier": {
         "@type": "PropertyValue",
         "propertyID": "ORCID",
         "value": "0000-0002-4863-0138",
         "url": "https://orcid.org/0000-0002-4863-0138"
       },
       "affiliation": [
         {
           "@type": "Organization",
           "name": "Center for Geospatial Information Science (CEGIS)",
           "url": "https://www.usgs.gov/centers/cegis"
         },
         {
           "@type": "Organization",
           "name": "NGTOC Rolla",
           "url": "https://www.usgs.gov/national-geospatial-technical-operations-center"
         }
       ]
     }
   ],
   "funder": [
     {
       "@type": "Organization",
       "name": "Center for Geospatial Information Science (CEGIS)",
       "url": "https://www.usgs.gov/centers/cegis"
     }
   ]
 }

}