U.S. Department of the InteriorU.S. Geological Survey

The National Map, Geospatial Ontology,and the Semantic Web

E. Lynn Usery

http://cegis.usgs.gov usery@usgs.gov

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Outline

Background – The National Map

The National Map OntologyA case of a Geospatial Ontology

Implementing The National Map on the Semantic Web

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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

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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 The National Map VisionVision

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The National MapThe 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

resolutionsAnalysis, modeling and other applications at

multiple scales and resolutions

The National Map is built on partnerships and standards

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The 8 Layers of The National Map

TransportationStructuresOrthoimageryHydrographyLand CoverGeographic NamesBoundariesElevation

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Nationwide Coverage 8 Data Layers

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MultiscaleGeneralization

Integrated Data

Authoritative Data Source

Nationwide Coverage 8 Data Layers

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User-Centered Design

E-Topo Maps

Intelligent Knowledge Base

Semantics-driven

Spatio-Temporal

OntologyDriven

Feature/Event Based

Quality Aware

MultiscaleGeneralization

Integrated Data

Authoritative Data Source

Nationwide Coverage 8 Data Layers

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TNM 1.0 TNM 2.0 TNM 3.0

Focus Data Information Knowledge

Data Layer based Integrated layers Feature and Eventbased

Data Model Theme based data models

Integrated data model

Intelligent semantic/spatial/ temporal model

Delivery Map and data products

Service oriented delivery

Intelligent knowledge base on Semantic Web

Services Viewer GeoServices Future Technologies & Services (e.g., semantics-driven, 3-Dcapabilities)

TNM Progression: Transitions

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Products of The National Map

Data display through The National Map viewerNew viewer, Palanterra, joint development from NGA, ESRI, and USGSViewer goes public Dec 3, 2009

Data download of 8 layersTopographic maps, 14,000 available now from USGS

Map Store, 3-year revision cycleNew 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

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Ontology for The National Map

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Feature Domains

EventsDivisionsBuilt-up areasEcological regimeSurface waterTerrain

Domains derived from ground surveys incorporated in DLG standards

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Terrain includes 58 USGS landform features

Aeolian Delta Island cluster QuicksandArch Dish Isthmus ReefBar Divide Karst RidgeBasin Drainage basin Lava Ridge lineBeach Dunes Lava Salt panBench Fault Mineral pile ShaftCape Floodplain Moraine SinkCatchment Fracture Mount Solution chimneysCave Fumarole Mountain Range SummitChimney Gap Peak TalusCirque Glacial Peneplain TerraceCliff Ground surface Peninsula ValleyCoast Hill Pinnacle VolcanoCrater Incline PlainCrater Island Plateau

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Ecological RegimeTundraDesertGrasslandScrubForestPastureCultivated CroplandTransition areaNature reserve

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Natural/ArtificialReach

hasPart: BottomChannel

PondBasin

Natural ArtificialMarine/Estuarine Freshwater Impounded Diked Channel Flow Control

Cove Watercourse Waterbody Reservoir Levee Siphon WeirForeshore Stream Lake Fish ladder Embankment Aqueduct LockFlat hasPart: Mouth Ice cap (regional) hasPart: Revetment Canal hasPart:Lock chamberIce field (regional) hasPart: Source Snow field (regional) Dam Flume hasPart: StramMarine Estuarine hasPart: Streambed Sastrugi (regional) Masonry shore Turning basin SpillwayOcean Estuary hasPart: Streambanks JettySea Bay hasPart: Crossing BreakwaterGulf Inlet hasPart: Ford Water intakeSubmerged Stream River PumpShore CreekhasPart: Shingle BrookShoreline ArroyoBeach RapidsIce floe (regional) BendPolyna (regional) Falls

CascadeWaterfallInnundation areaSpringMud potGeyserSlope springIce berg (regional)hasPart: Iceberg tongueGlacier (regional)Crevasse (regional)

WetlandMarshSwamp

Bog

Surface Water

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Built-up Transportation and warehousing 60Entertainment and Recreation 26Utilities 16Resource Extraction 13Structure 12Agriculture and Fishing 11Military 10Communication 7Waste Management 7Real Estate 6Place of Worship 6Manufacturing 4Institutions 3Burial Grounds 3Disturbed Surface 3Trade 3

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DivisionsCivil Units Boundaries

Cadastral Nation FencelineParcel Territory HedgePublic Land Survey System Tribal reservation PlaceLand grant State RegionHomestead entry County LocaleSurvey line Census Boundary linePrinciple meridian State Boundary pointBaseline County Hydrologic unitSurvey point Census county divisionPoint monument Block group ShippingSurvey corner Block Lane

Government unit TractTraffic separation scheme area

Municipality Special use zone Pilot waterCity Time zone RoundaboutTown Nature reserve Inshore trafic zoneVilliage Exclusive Economic Zone

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Events

Security Historical site

Hazard Hazard zone Military historyArcheological site

Earthquake IncidentHistorical marker Cliff dwelling

Flood Fire Tree Ruins

Area to be submerged Restricted area

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Ontology implementation

Classes established for all domain-level ontologies

Glossary of definitions from classesEstablishing axioms (in progress)Spatial relations

Working on predicates; some from OGCIdentifying which predicates are needed, which are in OGC, and which ones work

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Spatial Relations

Some relations are inherent in the class, e.g., bridge implies crossing

Most are applied when instances are integrated

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Geographical Scale

Ontological problemGeographic features exist in reality, but reality

cannot be separated from the observerOntology instances are consistent granularityQuantification of scale in representation

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Application

For The National Map, integrate ontology with the database schemasEach layer has a schema

Best Practices Data Model (transportation, structures, boundaries)

NHD data model for hydrographyFeatures from raster data in work

For example, terrain features from DEM and imagesEcological regimes?

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Task ontologies

User interfaceData integrationGeneralizationMap design and creation

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Developing a Semantic Data Model?

Current research Moving from existing Best Practices, NHD, and

raster data models to the Semantic WebCan database conversions to Semantic Web

accomplish this objective?

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Converting geospatial databases to the Semantic WebGNIS already loaded in RDFConverting Oracle databases in NHD and Best

Practices data models to RDF, RDFS, OWL, and other standards

Developing feature/event-based semantic data model

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Scenarios for use of The National Map in 2015

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Information Access and DisseminationWildfires are spreading rapidly across a San Diego mountainside. Fire fightershave deployed with two-way radios and Global Positioning Systems (GPS). In thecommand 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.

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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.

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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.

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Addressing the Presented Scenario

Immediate access to information based on common place nameIntuitive user interface, semantically-drivenAutomated 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 basisSpace and time changes incorporatedFeatures changed on transaction basisSemantics driven query and access

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Research needed to make the scenario possible from The National MapGeographic 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

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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

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Workshop concepts addressing needs of Ontology and Semantics of The National Map

Semantic Enablement Layer for OGC Web servicesTight Integration between space and semanticsWhat activity is allowed here? Spatial planning with

semanticsDesigning a geo-spatial application addressed to final-

users and based on Semantic Web2D geospatial indexing for proximity queries, extending

to 3D and 4D to support moving objects (MOBs)

U.S. Department of the InteriorU.S. Geological Survey

The National Map, Geospatial Ontology,and the Semantic Web

E. Lynn Usery

http://cegis.usgs.gov usery@usgs.gov