The U.S Geological Survey (USGS) is developing an ontology and semantic representation for topographic data and information. The ontology development has generated a taxonomy of all features on standard topographic maps, a formal machine readable vocabulary of feature names and definitions, predicates formed from attributes and relationships of the features, and actual instance data, including geometric coordinates and topological relations, encoded as predicates in a machine interpretable triple format. The geographic features are being developed as Resource Description Framework (RDF) triples with Uniform Resource Identifiers (URIs) and interlinked to become a part of Linked Open Data on the Semantic Web. We have developed semantics of vector- and point-based geographic data, including transportation, hydrography, boundaries, structures, and geographic names from basic tables of attributes and relationships of geographic features in these categories of data. We have created a graphical user interface to query these data with the SPARQL Protocol and RDF Query Language and to visualize these data in a cartographic rendering. The interface also supports data integration through federated queries of USGS data with other RDF and Linked Open Data available on the Semantic Web. Raster-based data including terrain, land cover, and orthographic images pose different requirements since no geographic features are readily defined and identified in these data. Through collaboration with ontologists and others at GeoVocamps, a set of basic ontology design patterns have been defined for terrain features. We are now beginning to use object-based image analysis and machine learning techniques, specifically neural networks, to automatically extract these features from basic data sources including lidar point clouds, elevation matrices, orthographic images, and scanned topographic maps. The extracted features are then built as RDF and become available through URIs, in the same manner as the geographic features built from the vector-based data. The ultimate goal of this work is to make all geographic features, currently shown on USGS topographic maps and in our databases of geographic information systems, available on the Semantic Web with each feature directly accessible through its URI.

About E. Lynn Usery

E. Lynn Usery is a Research Physical Scientist and Director of the Center of Excellence for Geospatial Information Science (CEGIS) with the U.S. Geological Survey (USGS). He has worked as a cartographer and geographer for the USGS for more than 25 years and a professor of geography for 17 years with the University of Wisconsin-Madison and the University of Georgia. Dr. Usery established a program of cartographic and geographic information science (GIScience) research that evolved into CEGIS. He has served as President of the University Consortium for Geographic Information Science (UCGIS) and the Cartography and Geographic Information Society (CaGIS) and is currently President of the American Society for Photogrammetry and Remote Sensing and Vice-President of the International Cartographic Association. He was editor of the journal Cartography and Geographic Information Science and is currently Associate Editor for the International Journal of Cartography. Dr. Usery is currently Chair of the Local Organizing Committee and Conference Director for the 2017 International Cartographic Conference which will be held in Washington, D.C. He is a Fellow of CaGIS and UCGIS and received the CaGIS Distinguished Career Award in 2012. Dr. Usery has published more than 100 research articles in cartography and GIScience. He earned a BS in geography from the University of Alabama and MA and Ph.D. degrees in geography from the University of Georgia. His current interests and research are in theoretical GIScience including geospatial ontologies and semantics, map projections, multidimensional data models for lidar, and high performance computing for spatial data.

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Ontology and Semantics for Topographic Information