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Semantic Annotation of Street-level Geospatial Entities

Semantic Annotation of Street-level Geospatial Entities. Nate Blaylock Institute for Human and Machine Cognition (IHMC) Ocala, Florida. Geospatial Language Understanding. Natural Language Understanding Ground references to paths, movement, orientation, and entities into lat/lon coordinates

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Semantic Annotation of Street-level Geospatial Entities

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  1. Semantic Annotation of Street-level Geospatial Entities • Nate Blaylock • Institute for Human and Machine Cognition (IHMC) • Ocala, Florida

  2. Geospatial Language Understanding • Natural Language Understanding • Ground references to paths, movement, orientation, and entities into lat/lon coordinates • Granularity • Most previous work at the city/state/country level (e.g., city names) • Our work at sub-city level (e.g., streets, businesses, parks)

  3. The PURSUIT Corpus • Recordings of on-the-road path descriptions • GPS tracks for ground truth • All references to geospatial entities (e.g., streets, intersections, businesses, bodies of water, etc.) hand annotated to GIS entity • Available at • http://www.cs.rochester.edu/research/cisd/resources/pursuit/

  4. VIDEO

  5. The TESLA Annotation Tool • Tool for human annotation of geospatial corpora • Synchronized playback of audio and GPS track in Google Earth • Search for entities in GIS databases

  6. GIS Resources • Google Local • API for search based on keyword and center point • TerraFly • Spatial Keyword Search (SKS) • Finer control of search based on keyword match, structured data • Recently released as open source • Data sets • Intersections, Street segments • Point data combined from a number of sources

  7. PURSUIT Corpus Data • 13 audio recordings (with GPS tracks) with almost 4 hours of audio • 1649 annotated geospatial entities

  8. Street-level geospatial semantics Needs (1) • Ontology needs • Streets as objects (not segments, overlapping names, orientation on street) • Intersections of various streets • General ontology of business types, buildings, etc.

  9. Street-level geospatial semantics Needs (2) • Knowledge base needs (instances) • Unavailable types: fire hydrants, water towers, fields, neighborhoods, parking lots, ... • Historical info: “old Courthouse”

  10. Street-level geospatial semantics Needs (3) • Qualitative reasoning/queries • Entities on a street: “all restaurants on left side of street X” • Street network: “all intersections allowing left turns on street Y” • Mixing of cardinal directions: substitute “north” for “left” above • Visibility: “I see a water tower in the distance”

  11. Ongoing Work • Information extraction • Path recovery • Entity geolocation • Dialog-based geolocation • “Tell me what you see and I’ll tell you where you are”

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