Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video
Direct estimation of physically interpretable periodic signals from raw video constitutes a spatiotemporally grounded learning problem that proves to be difficult especially when facing label sparsity, lack of physical grounding and standardization benchmarks. The wave monitoring at coastal sites is one such real-world example where current deep learning approaches for estimating wave parameters using video as input suffer from physical interpretability and require some kind of intermediate data processing. In this study we propose a framework for wave peak period estimation using only video as input through three components: automated region-of-interest detection using temporal pixel variance, multi-stage Sim-to-Real transfer learning process, and physics-guided regularization of the output predictions. Various spatiotemporal architectures, including Transformer and recurrent-convolutional were compared during the stages of synthetic pretraining, silver label adaptation, and expert fine-tuning. It has been found out that LtViViT achieves the highest accuracy in its estimates, while TinyWaveNet shows superior temporal stability and oceanographic skill. Additionally, ablation studies have demonstrated that physics-guided regularization helps to follow the trends in predictions more consistently and prevent physically meaningless predictions. Moreover, Grad-CAM-based explainability analysis of the physics-guided TinyWaveNet showed that its spatial focus aligns with hydrodynamically active surf-zone regions. Overall, the findings support physics-guided, video-based deep learning as a cost-effective and operationally viable approach for long-term coastal wave monitoring, and demonstrate a transferable strategy for physically-constrained spatiotemporal regression from video under data-scarce conditions.