1. U.S. Department of the Interior U.S. Geological Survey The National Map, Geospatial Ontology, and the Semantic Web E. Lynn Usery http://cegis.usgs.gov usery@usgs.gov

2. 2 Outline Background – The National Map The National Map Ontology A case of a Geospatial Ontology Implementing The National Map on the Semantic Web

3. 3 The National Map is a collaborative effort to improve and deliver topographic information for the nation The goal of The National Map is to become the nation’s source for trusted, nationally consistent, integrated and current topographic information available online for a broad-range of uses The National Map

4. 4  A seamless, continuously maintained, nationally consistent set of base geographic data  Developed and maintained through partnerships  A national foundation for science, land and resource management, recreation, policy making, and homeland security  Available over the Internet  The source for revised topographic maps The National Map Vision

5. 5 The National Map The National Map contributes to the NSDI The National Map includes eight data layers: transportation, structures, orthoimagery, hydrography, land cover, geographic names, boundaries, and elevation Public domain data to support USGS topographic maps at 1:24,000-scale Products and services at multiple scales and resolutions Analysis, modeling and other applications at multiple scales and resolutions The National Map is built on partnerships and standards

6. 6 The 8 Layers of The National Map Transportation Structures Orthoimagery Hydrography Land Cover Geographic Names Boundaries Elevation

7. 7 Nationwide Coverage 8 Data Layers

8. 8 Multiscale Generalization Integrated Data Authoritative Data Source Nationwide Coverage 8 Data Layers

9. 9 User-Centered Design E-Topo Maps Intelligent Knowledge Base Semantics-driven Spatio- Temporal Ontology Driven Feature/ Event Based Quality Aware Multiscale Generalization Integrated Data Authoritative Data Source Nationwide Coverage 8 Data Layers

10. 10 TNM 1.0TNM 2.0TNM 3.0 FocusDataInformationKnowledge DataLayer basedIntegrated layersFeature and Event based Data ModelTheme based data models Integrated data model Intelligent semantic/spatial/ temporal model DeliveryMap and data products Service oriented delivery Intelligent knowledge base on Semantic Web ServicesViewerGeoServicesFuture Technologies & Services (e.g., semantics-driven, 3-D capabilities) TNM Progression: Transitions

11. 11 Products of The National Map Data display through The National Map viewer New viewer, Palanterra, joint development from NGA, ESRI, and USGS Viewer goes public Dec 3, 2009 Data download of 8 layers Topographic maps, 14,000 available now from USGS Map Store, 3-year revision cycle New topographic map goes public Dec 3, 2009 – Example map, Altamont, Kansas Digital, georeferenced versions of all previous topographic maps for a specified 7.5-minute area

12. 12

13. 13 Ontology for The National Map

14. 14 Feature Domains Events Divisions Built-up areas Ecological regime Surface water Terrain Domains derived from ground surveys incorporated in DLG standards

15. 15 Terrain includes 58 USGS landform features AeolianDeltaIsland clusterQuicksand ArchDishIsthmusReef BarDivideKarstRidge BasinDrainage basinLavaRidge line BeachDunesLavaSalt pan BenchFaultMineral pileShaft CapeFloodplainMoraineSink CatchmentFractureMountSolution chimneys CaveFumaroleMountain RangeSummit ChimneyGapPeakTalus CirqueGlacialPeneplainTerrace CliffGround surfacePeninsulaValley CoastHillPinnacleVolcano CraterInclinePlain CraterIslandPlateau

16. 16 Ecological Regime Tundra Desert Grassland Scrub Forest Pasture Cultivated Cropland Transition area Nature reserve

17. 17 Natural/Artificial Reach hasPart: Bottom Channel Pond Basin NaturalArtificial Marine/EstuarineFreshwaterImpoundedDikedChannelFlow Control CoveWatercourseWaterbodyReservoirLeveeSiphonWeir ForeshoreStreamLakeFish ladderEmbankmentAqueductLock FlathasPart: MouthIce cap (regional)hasPart: RevetmentCanalhasPart:Lock chamber Ice field (regional)hasPart: SourceSnow field (regional)DamFlumehasPart: Stram Marine EstuarinehasPart: StreambedSastrugi (regional)Masonry shoreTurning basinSpillway Ocean EstuaryhasPart: StreambanksJetty Sea BayhasPart: CrossingBreakwater Gulf InlethasPart: FordWater intake Submerged Stream RiverPump Shore Creek hasPart: Shingle Brook Shoreline Arroyo Beach Rapids Ice floe (regional) Bend Polyna (regional) Falls Cascade Waterfall Innundation area Spring Mud pot Geyser Slope spring Ice berg (regional) hasPart: Iceberg tongue Glacier (regional) Crevasse (regional) Wetland Marsh Swamp Bog Surface Water

18. 18 Built-up Transportation and warehousing60 Entertainment and Recreation 26 Utilities16 Resource Extraction13 Structure 12 Agriculture and Fishing11 Military 10 Communication7 Waste Management 7 Real Estate 6 Place of Worship 6 Manufacturing4 Institutions 3 Burial Grounds 3 Disturbed Surface 3 Trade3

19. 19 Divisions Civil UnitsBoundaries CadastralNationFenceline ParcelTerritoryHedge Public Land Survey SystemTribal reservationPlace Land grantStateRegion Homestead entryCountyLocale Survey lineCensusBoundary line Principle meridianStateBoundary point BaselineCountyHydrologic unit Survey pointCensus county division Point monumentBlock groupShipping Survey cornerBlockLane Government unitTract Traffic separation scheme area MunicipalitySpecial use zonePilot water CityTime zoneRoundabout TownNature reserveInshore trafic zone VilliageExclusive Economic Zone

20. 20 Events SecurityHistorical site HazardHazard zoneMilitary history Archeological site EarthquakeIncident Historical markerCliff dwelling FloodFireTreeRuins Area to be submergedRestricted area

21. 21 Ontology implementation Classes established for all domain-level ontologies Glossary of definitions from classes Establishing axioms (in progress) Spatial relations Working on predicates; some from OGC Identifying which predicates are needed, which are in OGC, and which ones work

22. 22 Spatial Relations Some relations are inherent in the class, e.g., bridge implies crossing Most are applied when instances are integrated

23. 23 Geographical Scale Ontological problem Geographic features exist in reality, but reality cannot be separated from the observer Ontology instances are consistent granularity Quantification of scale in representation

24. 24 Application For The National Map, integrate ontology with the database schemas Each layer has a schema Best Practices Data Model (transportation, structures, boundaries) NHD data model for hydrography Features from raster data in work For example, terrain features from DEM and images Ecological regimes?

25. 25 Task ontologies User interface Data integration Generalization Map design and creation

26. 26 Developing a Semantic Data Model? Current research Moving from existing Best Practices, NHD, and raster data models to the Semantic Web Can database conversions to Semantic Web accomplish this objective?

27. 27 Converting geospatial databases to the Semantic Web GNIS already loaded in RDF Converting Oracle databases in NHD and Best Practices data models to RDF, RDFS, OWL, and other standards Developing feature/event-based semantic data model

28. 28 Scenarios for use of The National Map in 2015

29. 29 Information Access and Dissemination Wildfires are spreading rapidly across a San Diego mountainside. Fire fighters have deployed with two-way radios and Global Positioning Systems (GPS). In the command center, the new 3-D topographic maps overlaid with near real-time airborne color- infrared thermal imagery, real-time GPS wireless sensor data, and National Weather Service maps of wind direction, precipitation potential, and temperature displayed on the computers allow the command center team to tell the fire fighters through their two-way radios where the wildfire boundaries are and help them estimate the likely fire spread directions and speed in the next two hours. The operators at the command center find it intuitive to toggle between the various layers of data to analyze the situation, and can select different combinations to produce PDF files for fast printing to distribute to the crews. Meanwhile, the GPS and wireless communication enable the transmission of the position of the crew back to the command center, which has a large screen to display the overview maps with current positions of all firefighters and current fire perimeters. With a comprehensive GIS modeling technology and the information provided from The National Map (topography, slope, aspect, weather, soil moisture, vegetation, etc.), the command and control center calculates potential dangers for firefighters and immediately distributes a warning to the crews on the west side of the mountain to relocate 300 m farther west. Based on information from the overview maps, the center also dispatches another crew to the highest-risk zone and moves two more toward that zone. Their earlier participation in design phases are paying off in powerful but easy to use geospatial tools in a frantic and hostile environment.

30. 30 Integration of Data from Multiple Sources The San Diego fire is not yet contained. The crew assesses the current boundary of the fire, overlaid on the topographic map, which explains the difficulty of containing the spread up slope; however, there is still the unexplained spread to the east. The crew accesses the National Weather Service wind forecast, which is provided at a scale of 1:125,000 compared to the topographic map at 1:24,000. The crew invokes a tool for generalization of the topographic map to the smaller scale weather data, and a trend emerges. To determine high priority targets, the crew calls up an address directory and uses simple controls to geocode the addresses spatially on the fire map, showing location of structures in the fire’s path. To understand possible paths to fire sites, another layer with roads and another with trails are spatially matched (conflated) with the generalized map of topography. Finally, a remote sensing image with vegetation types is fused with the other layers to determine potential fuel loads for the fire path.

31. 31 Data Models and Knowledge Organization Systems A California regional dispatch operator gets a call about a new fire that has just been spotted in Sycamore Canyon. The caller further indicates that the fire is moving quickly up the west face of the canyon. The dispatcher does not know Sycamore Canyon or its location. Using a local geographic region profile to search the online The National Map, the dispatcher enters Sycamore Canyon and obtains a coordinate footprint of the canyon from The National Map Gazetteer. Using the returned footprint, the dispatch system zooms to the canyon’s location. The dispatcher selects an option within The National Map portal that uses the canyon footprint to automatically query geospatial databases housed in several different locations to obtain information on roads, streams, land cover, houses, and fire hydrants within the canyon. In addition, the dispatcher is able to select a 3D image of the canyon terrain that is offered as part of the initial query results. The dispatcher clicks the west wall of the canyon to select it and adds annotation that the fire was sighted moving rapidly up this face. The National Map portal seamlessly integrates the retrieved streams, roads, houses, and land cover onto the 3D display and the dispatcher sends the assembled dataset to the fire control and command center. With this information in hand, an emergency response team departs only minutes after the call was received.

32. 32 Addressing the Presented Scenario Immediate access to information based on common place name Intuitive user interface, semantically-driven Automated generalization and data integration (fusion, conflation) Explicit representation of a landform feature (canyon) as a queryable object in the database, and explicit definition Representation of landform feature parts as objects (canyon wall) Quality data on feature basis Space and time changes incorporated Features changed on transaction basis Semantics driven query and access

33. 33 Research needed to make the scenario possible from The National Map Geographic feature ontologies (hydrography, transportation, structures, boundaries, land cover, terrain, and image) Semantic geographic data models based on features and events from these ontologies, and an associated gazetteer replacing the Geographic Names Information System (GNIS) Ontology-driven generalization, data integration, user- interfaces, map generation Ontology-driven semantic data models for quality aware features and events supporting time, change, and semantics-driven transactions

34. 34 Workshop concepts addressing needs of Ontology and Semantics of The National Map Region Connection Calculus (RCC) in the Web Ontology Language (OWL) augmented by DL-safe rules is used in order to represent spatio-thematic knowledge Semi-automated semantic process for feature conflation that solves the type-matching problem using ontologies to determine similar feature types, and then uses business rules to automate the merge of geospatial features Generic categories to model the purpose of geography- related ontologies

35. 35 Workshop concepts addressing needs of Ontology and Semantics of The National Map Semantic Enablement Layer for OGC Web services Tight Integration between space and semantics What activity is allowed here? Spatial planning with semantics Designing a geo-spatial application addressed to final- users and based on Semantic Web 2D geospatial indexing for proximity queries, extending to 3D and 4D to support moving objects (MOBs)

36. U.S. Department of the Interior U.S. Geological Survey The National Map, Geospatial Ontology, and the Semantic Web E. Lynn Usery http://cegis.usgs.gov usery@usgs.gov