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 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

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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

Ontology

Driven

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 Event

based

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-D

capabilities)

TNM Progression: Transitions

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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

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Ontology for The National Map

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Feature Domains

Events

Divisions

Built-up areas

Ecological regime

Surface water

Terrain

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 Regime

Tundra

Desert

Grassland

Scrub

Forest

Pasture

Cultivated Cropland

Transition area

Nature reserve

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Natural/ArtificialReach

hasPart: BottomChannel

PondBasin

Natural Artificial

Marine/Estuarine Freshwater Impounded Diked Channel Flow ControlCove 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: Stram

Marine 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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Divisions

Civil Units Boundaries

Cadastral Nation Fenceline

Parcel Territory Hedge

Public Land Survey System Tribal reservation Place

Land grant State Region

Homestead entry County Locale

Survey line Census Boundary line

Principle meridian State Boundary point

Baseline County Hydrologic unit

Survey point Census county division

Point monument Block group Shipping

Survey corner Block Lane

Government unit TractTraffic separation scheme area

Municipality Special use zone Pilot water

City Time zone Roundabout

Town Nature reserve Inshore trafic zone

Villiage 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 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

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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 problem

Geographic features exist in reality, but reality cannot be separated from the observer

Ontology instances are consistent granularity

Quantification of scale in representation

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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?

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Task ontologies

User interface

Data integration

Generalization

Map 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 Web

Can database conversions to Semantic Web accomplish this objective?

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Converting geospatial databases to the Semantic WebGNIS 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

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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 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

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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

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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 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)

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