High-latitude coastal regions are highly sensitive to climate change, yet their geomorphology is obscured by sea ice, landfast ice and seasonal snow, restricting the applicability of optical remote sensing for fine coastal classification. To address this limitation, we develop an interpretable coastal classification framework integrating ICESat-2 photon-counting LiDAR and explainable machine learning. Multi-dimensional morphometric features describing cross-shore geometry, vertical relief and local slope variability are extracted from ICESat-2 ATL03 along-track profiles to train a CatBoost classifier, with five-fold cross-validation and sample weighting to mitigate class imbalance. Introducing SHAP-based interpretability into ICESat-2-driven coastal geomorphic classification enables the identification of morphometric controls on coastal-type differentiation. Validated in the Bering Sea with 447 profiles and a 75%/25% stratified split, the framework achieved an overall accuracy of 86.6%, a macro-average recall of 89.4% and a Kappa coefficient of 0.84. SHAP analysis identifies that coastal width is the most influential feature for model-based classification of coastal geomorphic types, while slope and local steepness variability serve as important predictive indicators for distinguishing rocky and sedimentary coasts. This framework links data-driven classification to geomorphic processes and provides a potentially generalisable approach for fine-scale coastal mapping in high-latitude environments.
Luan et al. (Sun,) studied this question.