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The National Map, Geospatial Ontology, and the Semantic Web. E. Lynn Usery. usery@usgs.gov. http://cegis.usgs.gov. Outline. Background – The National Map The National Map Ontology A case of a Geospatial Ontology Implementing The National Map on the Semantic Web. The National Map.

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the national map geospatial ontology and the semantic web

The National Map, Geospatial Ontology,and the Semantic Web

E. Lynn Usery

usery@usgs.gov

http://cegis.usgs.gov

outline
Outline
  • Background – The National Map
  • The National Map Ontology
  • A case of a Geospatial Ontology
  • Implementing The National Map on the Semantic Web
slide3

TheNational 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 nation’s source for trusted, nationally consistent, integrated and current topographic information available online for a broad-range of uses
slide4

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
the national map
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
the 8 layers of the national map
The 8 Layers of The National Map

Transportation

Structures

Orthoimagery

Hydrography

Land Cover

Geographic Names

Boundaries

Elevation

slide8

Generalization

Multiscale

Nationwide Coverage 8 Data Layers

Authoritative Data Source

Integrated Data

slide9

Feature/Event Based

User-Centered

Design

OntologyDriven

Generalization

E-Topo Maps

Multiscale

Nationwide Coverage 8 Data Layers

Authoritative Data Source

Integrated Data

Quality Aware

Spatio-Temporal

Intelligent Knowledge Base

Semantics-driven

products of the national map
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
feature domains
Feature Domains
  • Events
  • Divisions
  • Built-up areas
  • Ecological regime
  • Surface water
  • Terrain
  • Domains derived from ground surveys incorporated in DLG standards
ecological regime
Ecological Regime
  • Tundra
  • Desert
  • Grassland
  • Scrub
  • Forest
  • Pasture
  • Cultivated Cropland
  • Transition area
  • Nature reserve
ontology implementation
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
spatial relations
Spatial Relations
  • Some relations are inherent in the class, e.g., bridge implies crossing
  • Most are applied when instances are integrated
geographical scale
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
application
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?
task ontologies
Task ontologies
  • User interface
  • Data integration
  • Generalization
  • Map design and creation
developing a semantic data model
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?
converting geospatial databases to the semantic web
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
information access and dissemination
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.

integration of data from multiple sources
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.
data models and knowledge organization systems
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.
addressing the presented scenario
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
research needed to make the scenario possible from the national map
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
workshop concepts addressing needs of ontology and semantics of the national map
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
workshop concepts addressing needs of ontology and semantics of the national map35
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)
the national map geospatial ontology and the semantic web36

The National Map, Geospatial Ontology,and the Semantic Web

E. Lynn Usery

usery@usgs.gov

http://cegis.usgs.gov