# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 1 Introduction
It classifies the image into several regions according to the attributes of its pixels such as color or texture information [34]. Over the last decade, conventional image segmentation methods, such as thresholding, region-based, or hybrid segmentation, were often applied to segment water regions from the background [27; 35; 36]. For example, [PERSON] et al. [37] proposed a graph-based segmentation method where a virtual seed is used as guidance for flood region detection from images captured by the surveillance camera. Even though the graph-based and other segmentation methods proved to be capable of extracting flood information from the image, it is found that the methods are often handcrafted for specific data as prior knowledge of the data needs to be analyzed [28]. Hence, it may be difficult to apply these methods to other data images. Therefore, the segmentation task needs a state-of-the-art approach that can be adopted for all types of data complexity and can be applied to a large number of surveillance cameras. In recent years, deep learning approach has gained attention from researchers all over the world. It has been extensively applied in digital voice assistants, such as Siri, Google Assistant, and Alexa, as well as in self-driving cars. Deep learning is the key technology behind the autonomous vehicles that teaches computers to learn by example. Autonomous cars use deep learning semantic segmentation techniques to identify vehicles,pedestrians, traffic signs, and other road features. Hence, it has brought great changes and improvements in life with many impossible tasks now achievable using deep learning. With the evolvement of convolutional neural networks (CNN), many researchers have started applying this method in their studies as it can deal with large amounts of training data and achieve high accuracy results. However, since CNN models are data-hungry, a huge amount of data and computational power are needed. Thus, transfer learning is often used to address the issue of data scarcity and to improve the target task performance with less training time by using the pre-trained network. Transfer learning uses a pre-trained network as a starting point to learn a new task. Fine-tuning the pre-trained network is much easier and faster than training with randomly initialized weights from scratch. It is commonly used in deep learning applications, especially when dealing with a small number of training images. Past studies have proposed a variety of deep learning architectures for various tasks such as image classification [38; 39; 40], object detection [41; 42; 43], as well as semantic segmentation [44; 45; 46; 47]. In the field of flood applications, one of the early studies that used CNN to identify flooding areas was that of [PERSON] et al. [48]. The authors retrieved flooded regions from satellite images by using a segmentation network. A similar study was carried out by [PERSON] et al. [49], who used CNN to extract water bodies from multispectral data. The study used CNN and then a logistic classifier for water body extraction and compared the results with an artificial neural network (ANN) and support vector machine (SVM). The findings show that the proposed method achieved higher accuracies compared to ANN and SVM. In addition, a study on disaster detection from aerial imagery using CNN was done by [PERSON] and [PERSON] [50]. In this study, CNN was implemented in detecting the changes between pre-disaster and post-disaster imagery for both landslide and flood disasters. The proposed method obtained results between 80% and 90% of the f-score, which proved that CNN can detect a disaster region at relatively high accuracy. A similar study was carried out by [PERSON] et al. [51] that adopted the CNN approach to map surface water on Landsat imagery that could be used in predicting flood disasters. The trained model has successfully segmented water from land, snow, ice, clouds, and shadows and the model could be used in classifying other types of land cover that involve satellite imagery.