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Opening the Black Boxes in Ocean Mapping: Design and Implementation of the HydrOffice Framework

  • July 2019
DOI:10.13140/RG.2.2.18516.55681
  • Conference: AMSA 2019
  • At: Freemantle, WA, Australia
Authors:
Giuseppe Masetti at University of New Hampshire
Giuseppe Masetti
Tyanne Faulkes
Tyanne Faulkes
Tyanne Faulkes
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Brian Calder
Brian Calder
Brian Calder
  • This person is not on ResearchGate, or hasn't claimed this research yet.
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Abstract

Modern ocean mapping relies heavily on complex algorithms that may strongly affect the reported outputs (e.g., gridded bathymetry, acoustic mosaics). When implemented in commercial software, these algorithms usually cannot be directly examined, and thus represent black boxes. To ease the understanding of existing algorithms and the creation of better tools for ocean mapping, since 2016 the UNH Center for Coastal and Ocean Mapping and NOAA Office of Coast Survey have been developing an open research framework containing applications that cover all phases of the ping-to-public process. This effort, called HydrOffice, aims to facilitate data acquisition, to automate and enhance data processing, and to improve survey products. These themes are driving the creation of a growing collection of hydro-packages, each dealing with specific aspects of the ocean mapping workflow. The overall goal is to speed up the testing of new ideas and the Research-to-Operations (R2O) transition by minimizing the effort to develop and test new ideas. HydrOffice has developed a number of applications that encode both existing specifications and long-term best practices while enabling and extending recent discoveries and research-driven techniques. In return, many users have reported HydrOffice increases workflow efficiency, confirming the benefits of this approach.
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OPENING THE BLACK BOXES IN OCEAN MAPPING
DESIGN AND IMPLEMENTATION OF THE HYDROFFICE FRAMEWORK
G. MASETTI, T. FAULKES, B.R. CALDER
V0
FREEMANTLE, PERTH - JULY 11, 2019
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specs
manuals
tools
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CCOM/JHC IIM
NOAA PHB
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A framework of
libraries and tools
for Ocean Mapping
Quickly prototype
and test
innovative ideas
Ease the transition
from research to
operation
Ref.: G. Masetti, Wilson, M. J., Calder, B. R., Gallagher, B., and Zhang, C., “Research-driven Tools for Ocean Mappers”, Hydro Int., vol. 21, 5. GeoMares, 2017.
5
Support open formats
Listen the field feedback
Maintenance is a time sink
Support from hydro community
▪QC Tools
▪Sound Speed Manager
▪BAG Explorer
▪ENCx
▪Huddl
▪StormFix
▪SmartMap
▪Bress
▪CA Tools
▪OpenBST
▪…
HYDROFFICE APPS
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HYDROFFICE APPS
PYTHON SCIENTIFIC STACK
OCEAN MAPPING LIBS
& SCRIPTS
SOUND SPEED MANAGER
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Ref.: G. Masetti, Gallagher, B., Calder, B. R., Zhang, C., and Wilson, M. J., “Sound Speed Manager”, Int. Hydr. Review, vol. 17. IHB, pp. 31-40, 2017.
QC TOOLS
▪Assist Survey Review
and Chart Compilation:
▫Convert best practices
and specs into code.
▫Familiarize new
personnel to specs.
▪Routinely used by NOAA
OCS and other
professionals.
9
Ref.: Masetti, G., Faulkes, T., and Kastrisios, C., “Hydrographic Survey Validation and Chart Adequacy Assessment Using Automated Solutions”, U.S. Hydro 2019.
10
QC TOOLS →COMPLEMENTARY TOOLS
?
CA TOOLS
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Ref.: G. Masetti, Faulkes, T., and Kastrisios, C., “Automated Identification of Discrepancies Between Nautical Charts and Survey Soundings”, IJGI, vol. 7, p. 392, 2018.
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STORMFIX
ARTIFACTS
DETECTION
ARTIFACTS
REDUCTION
BACKSCATTER
MOSAICKING
ANGULAR
RESPONSE
ANALYSIS
Ref.: G. Masetti et al., “How to Improve the Quality and the Reproducibility for Acoustic Seafloor Characterization”, GeoHab 2017. p. Nova Scotia, Canada, 2017.
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JUST REMOVAL VS RANDOMIZATION SCHEMA
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PATCH-BASED VS. THEME-BASED ARA
Ref.: Fonseca, L. et al., “Angular range analysis of acoustic themes from Stanton Banks Ireland”, Applied Acoustics, vol. 70. pp. 1298-1304, 2009.
BRESS
▪Preliminary
segmentation from co-
located DEMs and
backscatter mosaics
▪Based on principles of:
▫Topographic openness
▫Pattern recognition
▫Texture classification
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Ref.: G. Masetti, Mayer, L. A., and Ward, L. G., “A Bathymetry- and Reflectivity-Based Approach for Seafloor Segmentation”, Geosciences, vol. 8(1). MDPI, 2018.
Landform ClassificationLocal Ternary Patterns Output SegmentsArea Kernels
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BSIP → OPENBST → ARCH ENGINE
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▪eLearning Python for Ocean Mapping » ePOM
▪Improve Cast Time/SmartMap prediction
▪Open Backscatter Toolchain » OpenBST
▪Real-time QC Tools » Mate
▪Expand QC/CA Tools » QAX
COMING TOOLS …
… and your ideas !!!
THANKS!
Visit us at: www.hydroffice.org
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PYDRO UNIVERSE STAND-ALONE APPS PYTHON PACKAGES
www.nauticalcharts.noaa.gov www.hydroffice.org GitHub/PyPi/Conda
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DISTRIBUTION
... Seafloor characterization is an important issue that has been recently discussed by [73], considering the possibility of auto classification, as well as cognitive interpretation of digital bathymetric data, in terms of geomorphological features [72,77,78]. ...
... The world of mapping and data visualization is quickly evolving into a three and four dimensional context, and the utility of LiDAR technology have been proven in a variety of scientific communities [78,79]. However, the challenge is to expand LiDAR awareness among non-technical audiences considering the high complexity of coastal areas and others who need information solutions that LiDAR can support. ...
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... However, due to the multitude of ways in which ocean mapping sensors are integrated, it is unrealistic to assume that these software applications will solve the variety of challenges that are present in ocean mapping. The ability to program can provide the ocean mapper with a more efficient mechanism to use available tools in new ways, allowing control for every single step of data analysis (Masetti et al., 2019). Software implementation of algorithms -paired with standards-compliant data-exchange formats -may represent a universal and portable solution for a specific task. ...
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The next generation of ocean mappers need to master programming skills to meet increasingly higher expectations for timely ping-to-public data workflows. As such, the e-learning Python for Ocean Mapping (ePOM) project was established at the Center for Coastal and Ocean Mapping/NOAA-UNH Joint Hydrographic Center (University of New Hampshire). The project aims to support new ocean mapping students and professionals in reaching a minimum level of programming skills. These skills are then expanded with further powerful capabilities by leveraging the open-source Python scientific stack and the NOAA (National Oceanic & Atmospheric Administration) Office of Coast Survey’s Pydro distribution. To the best of our knowledge, the ePOM project represents the first attempt at creating a set of comprehensive open-source courses providing students with the required initial coding skills for a career in the ocean mapping field.
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... Since its inception, the main aim has been to support the surveyors in fulfilling the accuracy and validity requirements of a modern survey workflow (Masetti et al., 2017). After its official deployment during the 2017 NOAA OCS field season, SSM has been adopted by several NOAA and UNOLS vessels, as well as by a number of professionals around the world Masetti et al., 2019;Sowers et al., 2019). ...
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  • Jan 2020
Despite recent technological advances in seafloor mapping systems, the resulting products and the overall operational efficiency of surveys are often affected by poor awareness of the oceanographic environment in which the surveys are conducted. Increasingly reliable ocean nowcast and forecast model predictions of key environmental variables – from local to global scales – are publicly available, but they are often not used by ocean mappers. With the intention of rectifying this situation, this work evaluates some possible ocean mapping applications for commonly available oceanographic predictions by focusing on one of the available regional models: NOAA’s Gulf of Maine Operational Forecast System. The study explores two main use cases: the use of predicted oceanographic variability in the water column to enhance and extend (or even substitute) the data collected on-site by sound speed profilers during survey data acquisition; and, the uncertainty estimation of oceanographic variability as a meaningful input to estimate the optimal time between sound speed casts. After having described the techniques adopted for each use case and their implementation as an extension of publicly available ocean mapping tools, this work provides evidence that the adoption of these techniques has the potential to improve efficiency in survey operations as well as the quality of the resulting ocean mapping products.
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ENSEMBLE DATA FITTING FOR BATHYMETRIC MODELS INFORMED BY NOMINAL DATA
Thesis
Full-text available
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Due to the difficulty and expense of collecting bathymetric data, modeling is the primary tool to produce detailed maps of the ocean floor. Current modeling practices typically utilize only one interpolator; the industry standard is splines-in-tension. In this dissertation we introduce a new nominal-informed ensemble interpolator designed to improve modeling accuracy in regions of sparse data. The method is guided by a priori domain knowledge provided by artificially intelligent classifiers. We recast such geomorphological classifications, such as ‘seamount’ or ‘ridge’, as nominal data which we utilize as foundational shapes in an expanded ordinary least squares regression-based algorithm. To our knowledge we are the first to utilize the output of classifiers as input into a numerical model. This nominal information provides meta-knowledge about seafloor creation and growth into our models implicitly. We performed two suites of experimental studies designed to clarify when these techniques add value. In our first study, we utilized the MergeBathy software for DBM construction to extensively investigate existing interpolators for feature-favoritism on different synthetic, idealized morphologies. This study reduced the possibility that the interpolators were a significant source of error in sparse data regions. Two feature-favoring interpolators then served as our nominal-informed interpolators and ensemble members. In our second study, we utilized Friedman’s hypothesis testing to verify that our nominally informed ensemble method outperforms splines-in-tension in the presence of sparse data. To our knowledge, this is the first comparison study of interpolation over sparse bathymetric data to verify statistically significant improvement in sparse-data regions.
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