Auto‐STIV: A Deep Learning Framework for Fully Autonomous Two‐Dimensional Streamflow Velocity Vector Field Estimation

Abstract

Accurate and continuous streamflow monitoring can support effective water resource management, yet substantial gaps persist in national streamgage networks due to operational challenges and costs. Non‐contact, image‐based techniques such as Space‐Time Image Velocimetry (STIV) offer a safe, scalable and, cost‐effective alternative for measuring surface flow velocity in rivers, which allows for streamflow calculation, especially during hazardous flood events. Traditional STIV methods, however, often require manual parameter tuning and struggle with complex flow conditions. This study introduces Auto‐STIV, a novel deep learning framework that fully automates the estimation of both flow direction and velocity magnitude from video imagery. The proposed approach utilizes a two‐stage Convolutional Neural Network model. The first stage predicts the primary flow direction by analyzing a stack of Space‐Time Images (STIs) generated from a series of candidate orientations. The second stage estimates the velocity magnitude from the STI corresponding to the predicted direction. The model was trained on a comprehensive data set collected under diverse, controlled laboratory conditions and evaluated using real‐world footage from the Colorado River. Results demonstrate that Auto‐STIV outperforms the traditional autocorrelation‐based STIV method, exhibiting higher accuracy and greater robustness in the presence of challenging environmental factors such as variable lighting, low‐contrast surface textures, and complex flow patterns. This research underscores the potential of deep learning to enhance the capabilities of image‐based velocimetry, that may support more reliable, scalable, and automated streamflow monitoring systems.