Deep Learning-Enhanced LSPIV for Automated Non-Contact River Surface Velocity Monitoring in Urban Channels

Reliable, real-time river flow monitoring is essential for disaster prevention, but traditional in situ methods are costly and high-risk. Large-scale particle image velocimetry (LSPIV) offers a non-contact alternative, though its accuracy is often compromised by noise and non-water pixels, requiring intensive manual data processing. This study proposes an integrated framework for enhancing non-contact river surface velocity estimation by combining deep learning-based water surface segmentation with optimized LSPIV, using accessible smartphone imaging. The framework was tested on two urban rivers in Taichung, Taiwan. DeepLabV3+ was identified as the superior segmentation model based on MPA/PA and MIoU metrics. The DeepLabV3+-derived mask was integrated into the LSPIV workflow, which was optimized using a 32 × 32 pixels interrogation area (IA), reducing processing time by approximately 44%. By removing non-water pixels, the masked LSPIV yielded a 7% increase in mean surface velocity. This suggests that the inclusion of non-water elements diluted the average, underscoring their tendency to introduce a low-velocity bias in unmasked calculations. The overall validation showed mean absolute percentage errors below 6% compared to the radar velocimeter. Consequently, this integrated smartphone-based framework offers a cost-effective and precise solution for future large-scale deployment in urban flood monitoring and smart city hydrological management.