The overall aim of this tool it to improve data quality issues, to reduce review and acceptance
times,
and ultimately to reduce ping-to-public times. Furthermore, once one of the developed algorithms
is mature and effective enough, existing commercial software might decide to adopt it
with a relatively easy transition based on the existing working implementation.
The speed in prototyping, a characteristic of the adopted Python language, eases the decision
to abandon a developed algorithms in case that is not effective or a commercially supported
implementation
becomes available.
QC Tools is written in Python (3.5+ supported), and the current stable release is
3.4.3.
Anomalous grid data (aka fliers) detected by flier finder are shown in 3D view.
The anomalous spikes are “lassoed” with a position stamp to facilitate prompt
detection and removal.
The “Flier finder” algorithm is dedicated to one of the major identified problem:
the quite common presence of anomalous data in the finalized gridded bathymetry
delivered
to the hydrographic branches (aka “fliers”). This represents a major concern since,
when fliers are found, there is considerable time and effort required to remove
them,
as it involves re-computation and re-finalization of the grids,
which can take several days (or longer) to accomplish with the additional
disadvantage
that the output is no longer the authentic field submission. This algorithm
contributes
to detect fliers as early as possible in the quality control process.
Its initial implementation scans gridded bathymetry and flags abrupt depth changes
as per user-set criteria, as shown in figure (white “lassos” encircle the anomalous
grid data).
Several algorithm modifications have also been testing (e.g., by including the
evaluation
of additional statistic layers provided by a CUBE DTM).
Feature validation
A wreck submitted in survey deliverables is represented by 1) a chosen least
depth sounding,
2) gridded bathymetry, and 3) an S-57 feature.
1, 2, and 3 must be in agreement for all features submitted to HB.
This is often not the case, so the developed algorithms scan these items and
flag discrepancies
for the hundreds (or sometimes thousands) of features submitted.
The “VALSOU to grid check” and “feature scan” algorithms have their focus on the
required agreement
between gridded bathymetry and submitted feature files, as well as
the adherence of those feature files to current specifications.
Wrecks, rocks, and obstructions should have appropriate representation in the
gridded bathymetry
with regards to position and least depth. It is a common situation at HBs to receive
surveys
with hundreds (or even thousands) of features that need to be manually checked
against the grid
to ensure agreement, and also to ensure proper attribution.
This process can be a massive time sink and, having a monotonous nature,
makes it perfectly suited for automation. The developed algorithms scan the gridded
bathymetry
and feature files to ensure this agreement, and that the attributes of the feature
are set
per current version of the NOAA Hydrographic Survey Specifications and Deliverables
(HSSD) manual (QC Tools),
or the current NOAA HCell Specifications (depending on which phase of the
ping-to-chart process
that the survey is in) (HCellScan tool). An example of the agreement we wish to
observe is shown
in figure.
QC Tools
Screenshot of QC Toools main window. User can drag and drop grid (BAG and Caris CSAR)
and S57 files to activate the processing tabs.
