Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 0 citations· 3 references
Abstract
Abstract. Mapping at the air–water interface in shallow coastal environments remains challenging due to the need to integrate heterogeneous datasets acquired under different geometric and operational conditions. This study presents a modular uncrewed surface vehicle (USV)-based system for simultaneous above- and underwater photogrammetric surveying supported by differential GNSS positioning. The system integrates a rigid multi-camera configuration, GNSS time synchronization, and a direct georeferencing workflow based on trajectory interpolation and lever-arm calibration. Experimental results from a rocky coastal site in Sardinia (Italy) show that underwater photogrammetry can achieve centimetric absolute accuracy (2–4 cm horizontally and ~8 cm vertically) without underwater ground control points. The USV enables controlled and repeatable acquisition in very shallow environments, while UAV photogrammetry complements the reconstruction of the emerged area. Limitations related to image quality and refraction effects are discussed. The system represents a flexible and scalable solution for integrated coastal mapping and monitoring.
The preservation of submerged cultural heritage depends on the ability to locate, document, and monitor sites before they are degraded or lost. Although the North American Great Lakes contain thousands of exceptionally well-preserved shipwrecks, their large geographic extent and diverse operating environments present significant challenges for efficient archeological survey. This study presents a multi-platform autonomous survey framework developed and implemented during 2021–2022 field campaigns in Lake Michigan and Lake Ontario. The framework integrates autonomous underwater vehicles (AUVs), autonomous surface vehicles (ASVs), crewed vessels, side-scan sonar, multibeam bathymetry, magnetometry, optical imaging, and field-based data review within a hierarchical workflow comprising wide-area assessment (WAA) reconnaissance, high-resolution geophysical (HRG) mapping, adaptive mission refinement, and visual confirmation. The surveys produced 19.72 km2 of geophysical coverage, including side-scan sonar mosaics, bathymetric surfaces, magnetic anomaly maps, and optical imagery that supported archeological interpretation. A case study from Lake Ontario demonstrates the framework’s effectiveness through the confirmation of a previously undocumented wooden shipwreck using complementary acoustic, magnetic, and visual datasets. Beyond the individual discoveries, the results demonstrate how integrated autonomous systems improve survey efficiency, support adaptive decision-making, and provide scalable methods for digital documentation, baseline site characterization, long-term monitoring, and preservation of submerged cultural heritage in freshwater and marine environments.
Abstract. Next to the known photogrammetric or acoustic measurement techniques, nowadays LiDAR is a promising option for high-resolution surveys or monitoring of underwater structures in low turbid and shallow waters. The objective of this investigation is to determine the performance of an underwater laser scanner and an air-borne bathymetric laser scanner for mobile data acquisitions. For this purpose, the underwater LiDAR (ULi) from Fraunhofer IPM is installed on a vessel. To enable the registration and georeferencing of the scan data, ULi is added by an Inertial Navigation System (INS) and two GNSS antennas. On the other hand, the air-borne bathymetric laser scanner (ABS) is integrated into an unmanned aerial system (UAS). Both systems are used to survey in the same measurement area to acquire comparable data sets. With the selected measurement settings, both systems can penetrate through water for more than 10 m and are able to resolve small underwater structures. Caused by shorter measurement ranges, ULi offers a point density which is approximately four times higher than the ABS and is able to resolve vertical underwater structures. The advantage of the UAS mounted ABS is that it can survey in shallow areas which cannot be accessed by vessels.
Annette Scheider, Sethmiya Herath Mudiyanselage, C. Werner et al.· The International Archives o...· 0 citations
Accurate bathymetric data are essential for the design and monitoring of coastal structures, but conventional multibeam surveys are costly and often impractical in shallow or confined areas. We evaluate a single-beam echosounder (SBES, ECT400) suspended beneath an unmanned aerial vehicle (UAV) as a rapid method with low logistical requirements for bathymetric monitoring of coastal infrastructure. Fieldwork was performed in an operational dry dock that was alternately drained and filled, enabling direct geometric validation against an ultra-high-resolution photogrammetric DEM (0.55 cm GSD). The co-registered dataset comprises N = 16,137 sonar returns to depths of ≈ 8 m. The UAV-mounted SBES produced a mean depth difference of 0.15 m (SD = 0.58 m) relative to the photogrammetric reference. From these residuals we estimate a 95% Minimum Detectable Change (MDC95) of ≈ 0.5 m when changes are assessed by aggregating repeated co-located passes. These results indicate that the UAV-SBES workflow is suitable as a Tier-1 screening tool for structural-health monitoring, effective for detecting metre- to decimetre-scale changes and triaging sites for targeted high-precision follow-up, but not for micrometre/mm-scale deformation monitoring. The method’s portability and vessel-free operation make it especially useful for frequent inspections in shallow, confined coastal settings.
Bethsaide Souza-Santos, M. Arza-García, J. Ortiz-Sanz et al.· Journal of Civil Structural...· 0 citations
Accurate spatial localization of small, transient targets in low-texture aquatic environments remains a fundamental challenge in UAV-based remote sensing, where open-water surfaces often lack stable tie points, degrading exterior orientation estimation and conventional photogrammetric georeferencing. An integrated UAV framework combining DG/AAT-BA georeferencing with deep-learning-based oriented bounding box (OBB) detection was implemented for high-precision localization, validated on the Critically Endangered Yangtze finless porpoise (YFP, Neophocaena asiaeorientalis) in the Yangtze–Poyang Lake system. The georeferencing component selects direct georeferencing (DG) in open-water scenes and automated aerial triangulation with bundle adjustment (AAT-BA) in feature-rich nearshore scenes. Validation using two static verification points showed that, relative to DG, AAT-BA reduced geometric georeferencing RMSE from 2.59 to 0.62 m under straight-flight conditions and from 3.61 to 0.67 m under turning-flight conditions. For target detection, a lightweight Laplacian edge-enhancement convolution module (LapConv) was incorporated into YOLO-OBB backbones, amplifying weak-edge and low-contrast features of partially submerged targets. Across four representative YOLO-OBB models and three group-constrained partitions, LapConv consistently improved the mean mAP@0.5, with gains of 0.026, 0.024, 0.019, and 0.026 for YOLOv8, YOLO11, YOLO12, and YOLO26, respectively. Applying this framework to six UAV missions across three ecologically and hydrologically distinct subregions enabled georeferenced mapping of porpoise distributions and visualized spatial distribution characteristics during the survey period. The approach is reproducible, minimally invasive, and potentially transferable to UAV-based monitoring of other small aquatic wildlife, providing a methodological basis for fine-scale spatial surveys and subsequent habitat analysis.
Dongxu Yang, Wanbing Ren, Yanren Li et al.· Drones· 0 citations
Abstract. Bathymetric Laser Scanning (BLS) enables high-resolution mapping of underwater topography using green-wavelength laser pulses that penetrate the water column. However, precise georeferencing of the BLS data is affected by refraction at the air–water interface, which displaces submerged features and affects conventional strip adjustment methods. This paper introduces an integrated refraction-aware georeferencing workflow that combines refraction correction with trajectory and boresight optimization within a unified adjustment framework. Implemented using the scientific OPALS laser scanning software, the workflow starts with direct georeferencing of uncorrected laser returns, derives a water surface model, applies Snell’s law-based refraction correction, and performs iterative strip adjustment until convergence. The approach was validated using UAV-borne topo-bathymetric LiDAR data from Lake Alm (Almsee) in Upper Austria, captured with a RIEGL VQ-840-GE sensor system. Comparative analysis across multiple processing scenarios demonstrates that the proposed integrated method significantly improves internal consistency between overlapping flight strips. The residual height discrepancies, quantified by the median absolute deviation (σMAD), were reduced from 4.5 cm using standard processing workflows to 2.1 cm with the integrated approach — an improvement exceeding 50%. A single processing pass was sufficient for the relatively calm conditions of the test site, though iterative refinement may benefit more dynamic water surfaces. The presented methodology is generic and can be embedded in any laser scanning framework supporting modular georeferencing and refraction correction.
Gottfried Mandlburger, Lucas Dammert, Jan Rhomberg-Kauert et al.· The International Archives o...· 0 citations
Coastal environments contain rich, largely unexploited geometric structure capable of providing globally referenced localization cues. In this work, we present two complementary localization frameworks that exploit shoreline and water-surface geometry for GPS-denied autonomous surface vessel localization. The first framework leverages LiDAR observations of the water surface to estimate roll, pitch, and heave (vertical motion), while recovering global position and heading through direct registration of shoreline observations against a satellite-derived coastline map. The second framework relies solely on passive imagery to detect the shoreline and horizon through semantic segmentation. Using the proposed coastal scene geometry, shoreline distance is inferred from monocular imagery. Shoreline observations are accumulated into short-duration local submaps, registered against the same satellite-derived coastline map, and fused within a hierarchical factor graph. Evaluated across three real-world coastal datasets, the LiDAR pipeline consistently improves trajectory accuracy over standard baselines, while the monocular architecture maintains bounded long-term drift. In addition, we establish that modern zero-shot foundation models can reliably extract shoreline observations across diverse coastal environments. Together, these results demonstrate that coastal geometry provides a powerful and dependable source of globally referenced information for GPS-denied maritime localization.