A novel method for coastal zone bathymetry based on multisensor data fusion and unmanned systems

Existing geospatial data fusion methods in hydrography do not take into account the accuracy of individual measurements when creating a bathymetric map. Consequently, geospatial data acquired by devices with low depth measurement accuracy may lead to a deterioration in the accuracy of coastal zone topography. To address this limitation, this study presents a novel method for coastal bathymetric monitoring based on the integration of multimodal geospatial data collected by unmanned platforms equipped with on-board sensors. These include Single-Beam Echo Sounder (SBES) and MultiBeam EchoSounder (MBES), a photogrammetric camera, and Light Detection and Ranging (LiDAR) from Airborne Laser Scanning (ALS) and Mobile Laser Scanning (MLS). As part of this method, bathymetric and photogrammetric data are processed using three modules: processing depth data, processing shallow-water data, and determining the coastline. After processing, the data are fused using an original weighted average data fusion method, in which weights for individual data sources are determined based on the measurement accuracy. The results demonstrate that the proposed coastal monitoring method effectively minimises redundant geospatial inputs. Notably, the model is parametric, and its accuracy depends on the appropriate selection of processing parameters and fusion settings.