Conference Agenda

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 \emph{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 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.

Coastal defense structures, such as breakwaters and groynes are an integral part of coastal engineering. These structures reduce the impact of waves and decrease beach erosion, but due to the constant forces to which they are exposed, repeated monitoring and evaluation is vital to the analysis of their structural integrity. However, coastal defense structures are most often located in the turbulent waters of the surf zone, which characteristics pose severe challenges for current methods. For example, waves pose challenges for image-based analysis, shallow-water limits sonar-based measurements, and currents, represent hazardous environments for surveying personnel. Here, recent advances in topo-bathymetric LiDAR have improved the ability to map data above and below the water surface within the same survey. In the field of structural engineering, point cloud data is already a commonly used information, and thus its applications in the monitoring of coastal defense structures present a natural extension of existing structural monitoring methods. Therefore, this study presents an automatic method for the detection of coastal defense structures with bathymetric LiDAR. The surveyed area consists of multiple groynes located along the Polish coast, which were surveyed using an airplane-based topo-bathymetric LiDAR scanner. The presented method then leverages the echo ratio and repeated clustering to extract the groynes from the data. We evaluate the extracted structures in comparison to manually annotated data. The results of this evaluation display a balanced accuracy of 92%, indicating an overall match with the reference data, but showing challenges and improvements for future work.

A state-of-the-art LiDAR sensor is assessed in terms of the accuracy, described as the sum of trueness and precision, of terrestrial and submerged points. The reference, against which the LiDAR data are evaluated, are conducted with a total station and can be assumed to show an uncertainty of less than 1 cm even for the submerged points. We find that the GNSS-based data set shows a systematic bias of about (-4, 7, 7) cm which can be defined as trueness and does not represent the quality of the LiDAR sensor but mostly of geo-referencing. The precision, which is a measure mostly influenced by the LiDAR sensor itself, is at 0.8 to 2.0 cm for terrestrial points and slightly worse with 1.1 to 2.6 cm for bathymetric points. Our study considers depths of up to 3 m and uses more than 300 points for the assessment.

Worldwide coastal areas comprise environmental triple points (air, land and seawater) that cope with coastal risks at unprecedented rates of change. Wind- and wave-related acute hazards add up to the chronic sea-level rise on interface zones that increasingly host human population and assets. Those societal challenges need to be overcome using the most discriminant and finest remote sensors.

We present an innovative two-step methodology to produce an ultra-high spatial resolution (UHSR) topobathymetry using a fusion of a RGB camera mounted on an aerial drone with a multispectral satellite imagery provided with very high temporal resolution. The fusion relied on a DJI Zenmuse P1 (0,08 m pixel size) borne by a DJI Marice 300 RTK, the PlanetScope SuperDove imagery, provided with eight bands at 3 m, and linear or nonlinear (neural network with two hidden layers endowed with three neurons, each) regression. Once the fusion achieved, both topography and bathymetry were mapped using, either the digital surface model (DSM) derived from the drone-derived photogrammetry, or the DSM combined with the UHSR SuperDove imagery. Both datasets served as predictors to model a digital topobathymetric terrain LiDAR response using linear or neural network regression.

The best drone-satellite fusion was completed by the bandwise neural network regression, ranging from R2test of 0,79 for the purple to 0,94 for the red edge band. The UHSR topobathymetry has been mapped by merging the topography and the bathymetry, distinctly predicted by the combination of the DSM with the UHSR Superdove imagery (R2test of 0,68 and 0,92, respectively).

Rocky coastal environments are ecologically important areas where land and sea processes interact in complex ways. Monitoring these zones is challenging, as they include steep cliffs, partially submerged features, and narrow transition areas where traditional surveying methods often struggle. Several European environmental directives now emphasize the need for regular observation of these coastal systems, increasing demand for practical and accessible surveying tools.

This work presents the development and initial testing of a small uncrewed surface vehicle (USV) designed to collect images above and below the water surface at the same time. The platform is based on a commercially available catamaran-style drone and carries two GoPro cameras mounted on a rigid vertical rod, with one camera positioned above the water and the other just below it. Both cameras are synchronized using GPS time, and the system incorporates a PPK-capable GNSS receiver for improved positioning. The payload is wireless and modular, allowing the platform to be deployed quickly.

The main contribution of the system is its ability to document the air–water boundary in a single pass, reducing issues related to changing meteorological and sea conditions. The paper also discusses how the platform was tested at a rocky site in Sardinia and outlines the types of data that can be obtained for environmental mapping.

The approach aims to offer a low-cost, flexible option for coastal monitoring.

Underwater laser scanners and air-borne laser scanners offer considerable potential for high-resolution monitoring of fine-scale underwater structures in shallow, clear waters. An underwater laser scanner mounted on a vessel is used for kinematic data acquisition in coastal waters. Additionally they are surveyed by an air-borne laser scanner. In this investigation, the resulting point clouds from both systems are analyzed in terms of their performance and achievable relative geometric quality.

The spatial accuracy and resolution of Multibeam Echosounder data are inherently lower than those of high-resolution underwater LiDAR measurements. However, while Multibeam Echosounder provides wide coverage and extensive historical availability, LiDAR is costly and covers relatively small areas. In this study, we propose an innovative approach to enhance Multibeam Echosounder resolution using a Super-Resolution Generative Adversarial Network with direct comparison to LiDAR data for accuracy assessment. The methodology involves converting Multibeam Echosounder data into grayscale format using various depth gradient techniques, analyzing differences in submarine geomorphology through calculations of slope and aspect, and evaluating statistical accuracy. The results show that the Super-Resolution Generative Adversarial Network model successfully improves Multibeam Echosounder resolution, producing data that closely correspond to LiDAR measurements, particularly in flat, sandy seabed areas. In contrast, regions with complex or rocky terrain exhibited more pronounced deviations, especially in aspect metrics, emphasizing the challenges associated with maintaining topographic orientation throughout the resolution enhancement process. The main conclusion is that enhancing Multibeam Echosounder data using Super-Resolution Generative Adversarial Network enables broader utilization of existing datasets to generate high-resolution models, offering a more cost-effective and accurate solution for seafloor mapping in areas where LiDAR data are unavailable.