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GEOSECTIONING DNC ® Data. Prototype and Development Project Phase V. David Turnbull 03 May 2006 NGA HydroVision Production Cell. Goals of Effort. Have an in-house automated capability to convert DNC ® data into a one-feature-one-time format to easily incorporate into any database.

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geosectioning dnc data


Prototype and Development Project

Phase V

David Turnbull

03 May 2006

NGA HydroVision Production Cell

goals of effort
Goals of Effort
  • Have an in-house automated capability to convert DNC® data into a one-feature-one-time format to easily incorporate into any database.
  • Create and demonstrate a new prototype version of the DNC® where it is one-feature-one-time.
  • Gain the many side benefits created by having these automated tools.
  • Geo-sectioning
  • One Feature One Time
  • New Attribution to DNC®
  • Generalization and SCAMIN
  • Common tile sizes
  • Common Schema
  • Libraries based on one or more 1 degree tiles
scamin scale minimum
SCAMIN (Scale Minimum)
  • SCAMIN is used in some degree with the international community with S-57.
  • SCAMIN is the least scale in which a feature should be portrayed
  • Currently based mostly on HACG scale ranges
    • Harbor: 0-49,999
    • Approach: 0-99,999
    • Coastal group 1: 0-249,999
    • Coastal group 2: 0-499,999
    • General: 0-2,500,000
loaded 29 cds into single database
Loaded 29 CDs into single Database
  • Development with Laser-Scan, Inc. (LSI) which gave Lamps2 an automated mass importer of DNC® CDs into a Gothic Object-Oriented (OO) Database
  • Loaded all 29 CDs into individual databases within the database in the NGA HydroVision Production Cell (NPC).
  • A single CD can be imported with a few clicks-
    • average time for a CD: 40 minutes
  • NPC has the latest of all the CDs loaded into the Gothic database
  • Also can run batch processing on multiple libraries
propagated into single dataset
Propagated Into Single Dataset
  • Chose the entire DNC17 since we had been maintaining it (last 3 phases as well)
  • All DNC17 data brought into a single dataset using a fully automated tool created within Lamps2
new attributes added during feature propagation
New Attributes Added During Feature Propagation
  • New attribution is per individual feature or object and added automatically
  • New attributes:
    • Library Name-Name of the original library
      • “DNC17_h1708210_Baltimore”
    • Chart Identifier-Chart number feature came from
    • Chart Edition Number-Edition of Chart
    • Chart Scale (NEW FOR THIS PHASE)
    • Hydrographic Datum (soundings only)
geo sectioned and generalized data
Geo-sectioned and Generalized Data
  • Originally manual
  • Now fully automated process (one click)
    • Step 1-initial SCAMIN attribute created and populated per feature based on library type.
    • Step 2- Pub Aid Number and Pub Number attributes added and populated from Text Attribute for Buoys, Beacons, and Lights
    • Step 3-Uses the Data Quality Areas (DQY) to spatially select the features and splits all line and area features at DQY boundaries.
geo sectioned the data 2
Geo-sectioned the Data (2)
  • Step 4- Process features by DQY and search within a defined radius to see if there are matching features of smaller scale. If there are, the SCAMIN attribute value is replaced with the smaller scale. (Point features only)
    • Lights-Additionally compares Publication Aid Number (PAN) and Range. If the range is not equal it looks at the PAN. If the PANs are equal it is a matching feature. If the PANs are not equal it produces a new QR light feature so that the compiler can visit it and determine if it is a different light or an error.
geo sectioned the data 3
Geo-sectioned the Data (3)
    • Step 5-Deletes unwanted, smaller scale, overlapped features.
  • Only the best scale features are kept within the DQY.
  • The data is now one-feature-one-time in a single dataset
created the new tiles
Created the New Tiles
  • Created Tiles within the new dataset using automated tile creator developed in the NPC using LULL, all tiles 1x1.
  • Areas which originally covered Harbors and Approach Datasets are now based on a 1 x 1 degree tile scheme.
  • Coastal and General areas are a maximum of 3 x 3 deg and a maximum of 9 1x1 deg tiles.
  • Tiles do not overlap, libraries do not overlap
  • Split data along new library boundaries (auto)
new datasets based on tiles
New Datasets Based on Tiles
  • Each library will be made up of either 1 or many tiles up to a max of 9.
  • DNC17 divided into 246 tiles, 60 datasets
  • The new datasets are named incorporating the tile name, ex: h17hjfk
    • the h so that it will meet spec. All libraries start with h (easier for us to code for now)
    • 17 for DNC17
    • hjfk for the tile that makes up the library
propagated data to datasets
Propagated Data to Datasets
  • Spatially queried each tile area and propagated the data from the single master dataset into each new tile library
  • Now fully automated for this phase
modified files for export
Modified Files for Export
  • Changed export scripts to effect all libraries to be exported
  • Allows for export of the new attributes and tile sizes, now automated
  • Easy to modify the code/schema to accomplish this.
exported data
Exported Data
  • All 60 libraries exported without viewing errors.
  • A cleanup process would be needed if we were to use it for production (edge-matching)
  • New attributes appeared properly structured within the VPF tables.
  • Created a Browse for the libraries (now automated)
  • Cut a CD of the new Prototype IV for DNC17.
test on ecdis platforms
Test on ECDIS platforms
  • Gave to SPAWAR to modify COGENT to view the data and utilize SCAMIN.
    • Data views utilizing SCAMIN.
  • Load into various ECDIS platforms
    • FUND: Testing
    • ECPINS®: Testing
    • ICE: Testing
    • VMS: Testing
demonstration of scamin
Demonstration of SCAMIN
  • Created customized code within Lamps2 to demonstrate the SCAMIN value being used to “thin out” the visible data when zooming out/in.
  • Proof of concept, the ECDIS would have to be modified to utilize the SCAMIN attribute as SPAWAR has.
  • We now can go to one-feature-one-time automatically in-house in order to populate the future NDME or NGA databases.
  • Proved that adding new attributes to the data properly will not kill the ECDIS functionality.
  • Size of the data on a CD is smaller.
    • Normal DNC17= 415 MB
    • Geo-sectioned DNC17= 274 MB
  • No duplication in collecting.
side benefits
Side Benefits
  • Have used it already for the foundation for the EPODS effort to supply one-feature-one-time data
  • Have used it to supply custom datasets to several customers (NAVO, TRANSCOM, HYSAS)
  • Utilized tools to create a One-Feature-One-Time Global DNC® coastline.
  • In process of using tools to create an ArcGlobe global DNC® model.
what is next phase v
What is next…Phase V
  • Further testing on ECDIS platforms
  • Finish display methods in Lamps2 to utilize the SCAMIN values for lines and areas.
  • Possibly Adding Chart limit feature class for Hardcopy.
  • Possibly creating a model using more SCAMIN bands similar to the IHO models
  • Possibly incorporating new agreed upon attributes from DNC2/Data Model/S57 efforts

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