Estimation of River Water Surface Elevation Using UAV Photogrammetry and
  Machine Learning
Radosław Szostak Marcin Pietroń Przemysław Wachniew Mirosław Zimnoch Paweł Ćwiąkała
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.