1. Variable Resolution Operational Implementation for 2018 Field Season
US Hydro 2019
Variable Resolution Operational Implementation for 2018 Field Season
NOAA is constantly striving to create value for the end user and customer of our products.
Create up-to-date, accurate products (Quality)
Create optimal number of in-demand products (Quantity)
Create products quickly (Efficiency)
Create high quality, useful products quickly while reducing time sinks and user interaction that can lead to waste and blunders, respectively (Value) VR
Grid data at highest, or user defined, resolution in one surface
3 Components
Resolution map, comprised of tiles of different resolutions,
Surface nodes, which are equally spaced within each resolution map tile,
Surface mesh, which describes the relationships between nodes
Input HDCS data
Generate resolution map
Nodes located
Node value computed and populated (CUBE, uncertainty, etc)
Resolution map and surface nodes stored as CSAR
Surface mesh created “on-demand”
3 Types
Depth Range: user defined table
Super grid tiles split based on user-defined criteria
Each tile assigned a resolution based on lookup table
Calder Rice (Density): CARIS adaptation of CHRT’s density-based resolution estimator
Each tile assigned a resolution based on target density
CARIS (Density): CARIS density-based resolution estimator
Not currently being used by NOAA; further testing
John Doroba, NOAA
Hydrographic Systems and Technology Branch
March 2019

2. Achieved Efficiency Gains
Reduce Surface Quantity
Remove Inconsistent Hypotheses R1 R2 R3 R4 R5
Reduce Designated Soundings
Remove Orphan Fliers
Surface Quantity
Each task performed on one surface
Designated Soundings
Highest resolution surface empirically supported by data density
Inconsistent Hypotheses
One surface, one hypothesis
Orphan Fliers
No depth gates clipping flier in another surface

3. Side Effects Adapt VR to QC Tools Holiday Finder
Grid QA & Flier Finder
Adapt SR tools for
VR CSAR/BAG
VR forced NOAA to automate = easier
VR forced us to adapt tools to VR.
Difficult to implement, Processing and analysis is easier

4. Anticipated Trade Off
Computing/Add To VR is Inefficient on Daily Basis
It takes longer to create a VR surface, but only one surface to create, review, recompute, and finalize.
Use SR for daily processing and management. One VR surface makes life easier on the back end.
Gains on back end (Complete surface generation/analysis)

5. Unexpected Hurdles VR Rendering was inhibitively slow
VR rendering was so slow. It couldn’t be used for manual cleaning or analysis.
Zoomed In (1x) – Zoomed Out (1x) – Panned (1/4 Map View to Right)
1:90,000 1:30,000 1:15,000 scale
Summed each action at each scale for each test
Tested
-Core Configuration
-Cache Size
-Rendering Method/Level of Detail
-NOS Baseline

6. NOAA VR Utilization NOAA utilized VR for 48% of processing pipeline
Used more on back end (Deliverables and Survey Review), than front end (Night Processing and Sheet Management)
Platforms in dynamic survey areas leveraged VR more than those in homogenous areas

7. VR Operational Implementation
Front End Inefficiency (Processing/Management)
Surface Rendering
Too long!
Surface Processing
Too long! Use SR for daily surface
Back End Efficiency (Deliverables/Survey Review)
One Surface
One surface analyzed
No turning surfaces on/off
No orphan flier or inconsistent hypotheses
Surface Rendering
-Panning and Zooming were slow
Surface Processing Times
-VR takes longer to compute than SR
-Computing / Add To is inefficient short term
One surface is easier to QC, analyze, and review

8. Change is Hard Ongoing challenges Efficient Cleaning/Review
Let VR help you
Let CUBE do it’s job
Examine “other” layers
Utilize QC Tools
CUBE does what it does, trust it
Use other layers like standard deviation or uncertainty to find anomalies
Let QC tools QC the data
VR makes this easier with one surface that is empirically supported by data density

9. Improvements Software Improvements Multiple threads process
Load Tile Grid
Separate
thread Rendering
Grid
~70% increase in rendering speed
CARIS modified rendering method and we saw a huge increase.
The 3 variables that have the most impact are:
VR rendering operations process
Rendering method / Level of detail
Rendering method has greater impact than level of detail
Cache size

10. Anticipate the Future
VR utilization will increase with increased rendering speeds
VR would be utilized more with decreased processing times
Infrastructure is critical, but don’t neglect software
VR will gain front end efficiency during 2019 season due to rendering modifications
VR will be used even more if processing times improve