Estimation of River Water Surface Elevation Using UAV Photogrammetry and Machine Learning

Abstract

Unmanned aerial vehicle (UAV) photogrammetry allows for the creation of orthophotos and digital surface models (DSMs) of a terrain. However, DSMs of water bodies mapped with this technique reveal water surface distortions, preventing the use of photogrammetric data for accurate determination of water surface elevation (WSE). Firstly, we propose a new solution in which a convolutional neural network (CNN) is used as a WSE estimator from photogrammetric DSMs and orthophotos. Second, we improved the previously known "water-edge" method by filtering the outliers using a forward-backwards exponential weighted moving average. Further improvement in these two methods was achieved by performing a linear regression of the WSE values against chainage. The solutions estimate the uncertainty of the predictions. This is the first approach in which DL was used for this task. A brand new machine learning data set has been created. It was collected on a small lowland river in winter and summer conditions. It consists of 322 samples, each corresponding to a 10 by 10 meter area of the river channel and adjacent land. Each data set sample contains orthophoto and DSM arrays as input, along with a single ground-truth WSE value as output. The data set was supplemented with data collected by other researchers that compared the state-of-the-art methods for determining WSE using an UAV. The results of the DL solution were verified using k-fold cross-validation method. This provided an in-depth examination of the model's ability to perform on previously unseen data. The WSE RMSEs differ for each k-fold cross-validation subset and range from 1.7 cm up to 17.2 cm. The RMSE results of the improved "water-edge" method are at least six times lower than the RMSE results achieved by the conventional "water-edge" method. The results obtained by new methods are predominantly outperforming existing ones.