The National Map, Geospatial Ontology, and the Semantic Web

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The National Map, Geospatial Ontology,and the
Semantic Web
E. Lynn Usery
usery_at_usgs.gov
http//cegis.usgs.gov
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Outline

• Background The National Map
• The National Map Ontology
• A case of a Geospatial Ontology
• Implementing The National Map on the Semantic Web

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The National Map

• 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
  nations source for trusted, nationally
  consistent, integrated and current topographic
  information available online for a broad-range of
  uses

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The National Map Vision

• 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

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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 124,000-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
Transportation Structures Orthoimagery Hydrography
Land Cover Geographic Names Boundaries Elevation
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Nationwide Coverage 8 Data Layers
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Generalization
Multiscale
Nationwide Coverage 8 Data Layers
Authoritative Data Source
Integrated Data
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Feature/Event Based
User-Centered Design
OntologyDriven
E-Topo Maps
Generalization
Multiscale
Nationwide Coverage 8 Data Layers
Authoritative Data Source
Integrated Data
Quality Aware
Spatio-Temporal
Intelligent Knowledge Base Semantics-driven
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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
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Ecological Regime

• Tundra
• Desert
• Grassland
• Scrub
• Forest
• Pasture
• Cultivated Cropland
• Transition area
• Nature reserve

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Surface Water
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Built-up
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Divisions
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Events
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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
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

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Scenarios for use of The National Map in 2015
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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.
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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 1125,000 compared to the topographic
  map at 124,000. 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 fires 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 canyons 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)

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The National Map, Geospatial Ontology,and the
Semantic Web
E. Lynn Usery
usery_at_usgs.gov
http//cegis.usgs.gov