Multisensor Data Fusion and Deep Feature-Based Classification for Mapping China’s Coastal and Wetland Areas

Ecosystem stability, economic growth, and climate regulation are all greatly aided by coastal and wetland regions. This is particularly true in rapidly evolving regions, such as China&#x2019;s extensive coastline. Environmental monitoring, shoreline change detection, and sustainable coastal zone management all depend on a precise definition of coastal limits. In this work, we present a deep learning-based method for binary coastline segmentation that combines both deterministic and probabilistic models, utilizing unmanned aerial vehicle (UAV)-derived data. We build and evaluate a residual U-Net and a Bayesian U-Net architecture, the former using Monte Carlo dropout for uncertainty quantification. Derived from UAV video frames and annotated using Label Studio, the collection comprises high-resolution pictures fit for fine-grained coastal edge identification. While the Bayesian U-Net gave equivalent segmentation performance [<italic>F</italic>1 &#x003D; 0.78, intersection over union (IoU) &approx; 0.65] along with calibrated confidence estimates, the residual U-Net obtained a best validation <italic>F</italic>1-score of 0.812 and IoU of 0.686. The expected calibration error (ECE) was used for model calibration; the Bayesian U-Net achieved an ECE as low as 0.0007 with temperature scaling (<italic>T</italic> &#x003D; 10). Consistent with the desired probabilistic behavior, uncertainty histograms showed that the model is more uncertain during erroneous predictions than during accurate ones. These results demonstrate the effectiveness of deep learning and uncertainty-aware modeling for UAV-based coastal mapping, highlighting its potential in decision-making systems that require trust, interpretability, and high-resolution coastal monitoring. By combining Bayesian modeling in coastal remote sensing with UAV-based photography, the proposed framework advances the expanding area of multisensor data fusion.