# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 1 Introduction  
The frequency and impact of flood disasters have continued to escalate, causing losses of human life and damages to property due to extreme changes in climate events. The effects of extreme weather have been observed in many countries. For instance, in the UK, a series of flooding events occurred in late 2013 and early 2014, caused by continuous major storms hitting the UK, eliciting a major emergency response and media exposure [1]. Malaysia also experienced an increase in rainfall extreme in December 2014, which led to the worst flooding in decades [2], affecting more than 200,000 residents and more than RM2.5 billion economic losses [3]. In recent years, Brazil experienced heavy rainfall, setting new rainfall records at certain locations [4]. For instance, Santo Agostinho had the wettestperiod in 110 years while Sao Paulo experienced the wettest period in 77 years in January 2020, causing 100 deaths. There were 367 disaster events on average per year worldwide, with floods accounting for 44% of total events [5]. The flood issue has gained attention globally with significant efforts being made to develop effective flood prevention and monitoring solutions. Currently, the primary approaches to monitoring flood events based on data sources used are sensor measurements and remote sensing [6; 7; 8; 9; 10; 11]. The common flood monitoring system obtains data from gauge stations such as water level stations and rainfall stations [12; 13]. The on-site stations use sensors that allow real-time measurement of water levels or rainfall levels. The data are then used to forecast flood events by developing dynamic flood modeling that provides advanced flood information for flood management and mitigation. However, the main restriction of using these sensors is the limited number of sensor installations due to the high cost of installation and maintenance [14]. Besides, the water level sensor provides only point-based information and the data cannot be visualized, thus it does not accurately represent the actual condition at sites. Remote sensing technology is another option to monitor flood events instead of direct sensor measurements. Optical imagery and radar imagery are usually adopted for monitoring floods within a large-scale area that can cover the entire flood events [15; 16; 17; 18; 19]. However, it is not easy to extract information within a specific small area from satellite imagery. Besides, satellite images are normally affected by vegetation canopies and cloud covers which require post-processing work before the data can be used. Time delay due to complex processing work and satellite orbital cycles are other restrictions pertaining to the use of satellite imagery in flood applications. On the other hand, unmanned aerial vehicles (UAV), another option in remote sensing, can provide more detailed information in specific, small or large flood areas [20; 21; 22]. UAVs could be operated autonomously using a pre-programmed flight plan, which is a wise choice during flood events. However, when it is being operated during poor weather conditions, especially during heavy rain and windy conditions, the performance of a UAV could be affected in terms of data accuracy and resolution [23; 24; 25; 26]. Due to the limitations in current methods used for flood monitoring, the surveillance camera has become a popular alternative to be used as an input source for flood monitoring events, especially in a small-scale areas [14; 27]. The information obtained from the surveillance camera could be in still images [28; 29] or video footage [30; 31] and could be extracted using various computer vision techniques. Computer vision is the common approach to capture and process the data from a surveillance camera for flood disaster applications by employing a visual sensor [32; 33]. For instance, image segmentation is the common computer algorithm used to extract useful information from digital images.