ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XI-2-2026
https://doi.org/10.5194/isprs-annals-XI-2-2026-595-2026
https://doi.org/10.5194/isprs-annals-XI-2-2026-595-2026
03 Jul 2026
 | 03 Jul 2026

Automated classification of coastal defense structures using airborne bathymetric LiDAR

Jan Rhomberg-Kauert, Lucas Dammert, Gottfried Mandlburger, Tomasz Kogut, Arkadiusz Tomczak, and Małgorzata Jarząbek-Rychard

Keywords: coastal management, tides, currents, waves, infrastructure, groynes

Abstract. 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, 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 existing methods. For example, waves pose challenges for image-based analysis, shallow-water limits the efficiency sonar-based measurements, and currents make for 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 commonly used, 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 topo-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 automatically 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 96%, indicating an overall match with the reference data, but showing challenges and improvements for future work.

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