Field Application of Terrestrial and Vessel-Based LiDAR with AI-Assisted Point-Cloud Processing for Sustainable Coastal Feature Extraction and Shoreline Management

Accurate characterization of coastal environments requires high-resolution spatial data and robust analytical workflows which are capable of capturing the complexity of intertidal surfaces and engineered shoreline structures. This study presents a field application of terrestrial and vessel-based LiDAR with AI-assisted point-cloud processing to support coastal mapping and shoreline analysis in a complex tidal flat environment. Field measurements were conducted in a geomorphologically dynamic estuarine system dominated by wide tidal flats and diverse natural and artificial coastal features. A comparative assessment of terrestrial laser scanning (TLS) and vessel-based mobile mapping system (MMS) data revealed distinct platform characteristics: TLS provided long-range, high-fidelity geometric measurements suitable for broad intertidal zones, whereas vessel-based MMS offered rapid and continuous coverage but exhibited range-related limitations in offshore and distal areas. To extract key coastal features, an AI-assisted workflow was implemented in Trimble Business Center (TBC), primarily using the TLS dataset as the input for feature extraction. The vessel-based MMS data were used to evaluate complementary acquisition characteristics, including coverage continuity, scanning range, accessibility, and visualization of coastal features, rather than as direct input for joint TLS–MMS AI classification. A manually curated training dataset consisting of 80 object-level point-clouds—cars, streetlights, powerlines, and fences—was used to train a custom extraction model, while TBC’s built-in AI tools were employed to automatically separate ground surfaces, vegetation, and built structures from the TLS point-cloud. The TLS-based AI-assisted workflow produced a multilayered representation of the TLS data, enabling detailed delineation of intertidal flats, engineered shoreline structures, and adjacent artificial objects. Quantitative evaluation based on comparison with manually annotated ground truth yielded an overall F1-score of approximately 0.87, indicating practical extraction performance for the evaluated object types. The results highlight the complementary strengths of TLS and vessel-based MMS data and the practical applicability of TLS-based AI-assisted point-cloud processing in complex coastal settings. The results indicate that TLS-based AI-assisted point-cloud processing, supported by vessel-based MMS comparison, can provide an operationally useful approach for coastal feature extraction, shoreline monitoring, and coastal infrastructure assessment in complex tidal flat environments. By improving the acquisition, interpretation, and management of high-resolution coastal spatial information, the proposed workflow can support sustainable shoreline monitoring, coastal infrastructure maintenance, and evidence-based coastal environmental management in vulnerable tidal flat environments.