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\begin{document}

\title{Eye of Horus: A Vision-based Framework for Real-time Water Level
Measurement}



\author[1]{Seyed Mohammad Hassan Erfani}%
\author[1]{Corinne Smith}%
\author[1]{Zhenyao Wu}%
\author[2]{Elyas Asadi Shamsabadi}%
\author[1]{Farboud Khatami}%
\author[1]{Austin R.J. Downey}%
\author[1]{Jasim Imran}%
\author[1]{Erfan Goharian}%
\affil[1]{University of South Carolina}%
\affil[2]{University of Sydney}%


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  \date{May 04, 2024}


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\begin{abstract}
Heavy rains and tropical storms often result in floods, which are
expected to increase in frequency and intensity. Flood prediction models
and inundation mapping tools provide decision-makers and emergency
responders with crucial information to better prepare for these events.
However, the performance of models relies on the accuracy and timeliness
of data received from in-situ gaging stations and remote sensing; each
of these data sources has its limitations, especially when it comes to
real-time monitoring of floods. This study presents a vision-based
framework for measuring water levels and detecting floods using Computer
Vision and Deep Learning (DL) techniques. The DL models use time-lapse
images captured by surveillance cameras during storm events for the
semantic segmentation of water extent in images. Three different
DL-based approaches, namely PSPNet, TransUNet, and SegFormer, were
applied and evaluated for semantic segmentation. The predicted masks are
transformed into water level values by intersecting the extracted water
edges, with the 2D representation of a point cloud generated by an Apple
iPhone 13 Pro LiDAR sensor. The estimated water levels were compared to
reference data collected by an ultrasonic sensor. The results showed
that SegFormer outperformed other DL-based approaches by achieving
99.55\% and 99.81\% for Intersection over Union (IoU) and accuracy,
respectively. Moreover, the highest correlations between reference data
and the vision-based approach reached above 0.98 for both the
coefficient of determination (R2) and Nash-Sutcliffe Efficiency. This
study demonstrates the potential of using surveillance cameras and
Artificial Intelligence for hydrologic monitoring and their integration
with existing surveillance infrastructure.%
\end{abstract}%



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