Presentations for the Canadian Hydrographic Conference
Bibliographic record
Abstract
Casey O'Heran Title: Horizontal Calibration of Vessel Lever Arms Using Unmanned Aircraft Systems (UASs) Knowledge of lever arm distances from sonars, mounted on vessels, to systems such as Inertial Measurement Units (IMUs) and Global Navigation Satellite Systems (GNSS) is crucial for accurate ocean mapping applications. Traditional methods, such as laser scanners or total stations, are used to determine professional survey vessel lever arm distances reliably. However, for vessels of opportunity that are collecting volunteer bathymetric data, it is beneficial to consider survey methods that are less time consuming, less expensive, and which do not involve bringing the vessel into a dry dock. With the development of Unmanned Aircraft Systems (UASs) in the field of mapping, more cost-effective and quicker surveys can be conducted. To investigate the feasibility of conducting accurate horizontal lever arm surveys of vessels, while maximizing time efficiency in data collection, UAS surveys of a vessel with calibrated lever arm distances were conducted using both Structure for Motion (SfM) photogrammetry and aerial LiDAR while the vessel was docked at the pier. Estimates of the horizontal uncertainties, for both methods, were obtained by comparing the horizontal distances between targets acquired by the UAS methods to ground-truth measurements of lever distances from survey-grade laser scanning of the vessel. With the use of Ground Control Points (GCPs), horizontal uncertainties of both the photogrammetry and LiDAR models are on the order of centimeters, with the LiDAR model being slightly higher in horizontal uncertainty than most of the photogrammetry models. Ivan Guimaraes Title: Calibrating Broadband Multibeam Seabed Backscatter Standard calibration procedures for multibeam sonars currently only address the fidelity of the bathymetric data. Equivalent effort is needed to ensure that the acquired seabed backscatter strength measurements are referenced to a similarly precise level. This project presents an operational method utilizing multiple pre-calibrated split beam echo sounders covering a wide range (50-400 kHz) of frequencies. This is needed to cover the full range of frequencies utilized by multi-sector multibeams operating in continental shelf depths. Roland Arsenault Title: A mapping focused open-sourced software framework for Autonomous Surface Vehicles A software framework, dubbed “Project 11”, was developed as a backseat driver for Autonomous Surface Vehicles (ASVs). Key design features include the ability to quickly and easily specify survey plans; monitoring of mission progress, even over unreliable wireless networks; and to provide an environment to develop advanced autonomous technologies. Presenter Bio Casey O’Heran earned his B.S. in Surveying Engineering from Ferris State University in Big Rapids, Michigan. During his time there, he participated in projects that exposed him to geospatial data acquisition using land surveying methods and data fusion. His studies also exposed him to the theory of multispectral LIDAR, echo sounders, and their ability to collect bathymetric data. This exposure sparked his interest in bathymetric data acquisition and ocean mapping. Casey is now pursuing a master’s degree in Ocean Engineering with a focus on Ocean Mapping. This experience positions Casey to utilize developed skill sets in both terrestrial land surveying and hydrographic sciences in the open market. Ivan Bodra Guimaraes graduated with a degree in Naval Sciences–Major Electronics from the Brazilian Naval Academy in 2008, and specialized in Hydrography (Cat. "A") in the Directory of Hydrography and Navigation of the Brazilian Navy in 2011. He worked on Brazilian hydrographic researcher ships from 2009 to 2018, operating with singlebeam and multibeam echosounders and sidescan sonar in offshore, coastal, and shallow waters, and rivers. Ivan will be working mainly with tides, as that is an important aspect to be considered in hydrographic surveys on shallow and coastal waters and on rivers, at some point. Roland Arsenult joined CCOM/JHC in 2000. He received his Bachelor's degree in Computer Science and worked as a research assistant with the Human Computer Interaction Lab at the Department of Computer Science, University of New Brunswick. As a member of the Data Visualisation Research Lab, he combines his expertise with interactive 3D graphics with his experience working with various mapping related technologies to help provide a unique perspective on some of the challenges undertaken at CCOM/JHC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.502 | 0.136 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".