# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 1 Introduction
In addition, [PERSON] et al. [52], [PERSON] et al. [53], and [PERSON] et al. [54] performed similar studies that applied deep learning for water body extraction from high-resolution satellite imagery. Even though they used different CNN architectures, all findings show that deep learning performed well as compared to conventional approaches. Despite many deep learning studies that have been performed on remote sensing imagery in flood applications, there are only a few studies that used the deep learning approaches, in particular water segmentation methods, to exploit digital images, especially from the surveillance camera. A study carried out by [PERSON] et al. [29] used a deep learning technique to extract flooded regions from surveillance camera images. This work performed several algorithms for water segmentation, namely FCN-8s, DenseNet, and Pix2 Pix, to monitor river water levels during flood events to determine the efficiency of each algorithm. Next, [PERSON] et al. [55] applied a deep convolutional neural network to detect floodwater and introduced a static observer flooding index (SOFI) to determine water level fluctuations. Water segmentation in this work was developed based on U-net architecture and obtained IoU higher than 90% on average. Successful work has been done by [PERSON] et al. [56] that applied CNN based on SegNet network to segment river water from camera images. The authors installed the camera in one location and then captured images for 50 consecutive days to represent different conditions and ambient. The images were then labeled and used as the input dataset for the network. The performance of SegNet achieved around 97% and 98% for pixel accuracy and IoU metrics, respectively. Regardless of the high performance, the author did not apply the trained network on images from different locations, which is why the results from this study were not included for comparison. [PERSON] et al. [57] investigated the use of deep learning for water levels estimation from the surveillance images. The authors utilized UperNet network and DeepLabV3 network for the water segmentation process and reported that DeepLabV3 obtained better performance than the UperNet network. Then, water levels were estimated using landmark-based water-level estimation (LBWLE) and SOFI index [55]. Based on the previous studies conducted, it can be concluded that [PERSON] et al. [55] and [PERSON] et al. [57] only focused on water level fluctuation and estimation, whereas this paper highlights water level thresholds that consist of normal, alert, warning and danger levels for flood alert. In particular, this paper explores the use of transfer learning approaches for image segmentation, in the context of flood detection. In this work, two powerful semantic segmentation networks, DeepLabv3+ and SegNet, have been adopted to extract the water body regions from the surveillance images. DeepLabv3+ network was created with weights initialized from a pre-trained Resnet-18 network, while the SegNet model adapted the VGG-16 network [58] for segmenting the water bodies. The networks were trained, tested, and evaluated to determine their performance. During the inference phase, new images from several locations were used on the networks to segment the water regions from the background to find out how widely applicable the networks are. The network that shows better performance was then used to segment a series of images captured for five consecutive days in one specific location. The segmentation results were extracted and post-processing steps took place to enhance the water features. Next, water level information generated from light detection and ranging (LiDAR) data were overlaid with the post-processed image to estimate the river water level, determine the threshold levels, and observe the fluctuation of water level in the study area visually.