# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 4 Discussion  
Flood monitoring and management mostly rely on data obtained from in-situ sensor measurements such as rainfall and water level sensors. The means provide almost real-time transmission of one-dimensional measurement data at a high level of accuracy. However, one spatial dimension values are not enough to represent the spatial dynamics of the surface water extent. The lack of dynamic information leads to the difficulty in understanding the field situation by decision-makers who need to make proper plans and take appropriate actions in reducing the impacts of flood events. This work proposed to apply semantic segmentation methods for water body detection using images captured by surveillance cameras. By using these methods, a static surveillance camera could be changed into an active visual sensor that brings benefits, especially in analyzing the real situation on-site in the context of flood disasters. Semantic segmentation architecture is generally composed of encoder and decoder networks, where encoder performs downsampling to distinguish classes while decoder performs the upsampling operation to learn dense prediction. In particular, two state-of-the-art segmentation networks were employed in this work, which are SegNet and a recent segmentation network known as DeepLabv3+ networks. These two networks were selected based on their superior performances demonstrated in previous studies [71; 72; 73; 74; 75; 76]. In a recent study, [PERSON] et al. examined several semantic segmentation models including SegNet and DeepLabv3+ for smoke segmentation. It was reported that both models achieved competitively promising performances and obtained better evaluation results as compared to other segmentation models. Hence, this work exclusively discussed the two semantic segmentation models. The findings show that both trained models could be used in water segmentation but the model trained using the DeepLabv3+ network outperformed the one that used SegNet in terms of evaluation metrics. In particular, DeepLabv3+ obtained more accurate water segmentation and smooth water regions delineation as compared to SegNet. The results aligned with previous studies as DeepLabv3+ consists of an effective decoder module that helps in retrieving detailed object boundaries of the segmentation results [77; 78; 61; 79]. However, when applying the DeepLabv3+ model to a series of five-day images in the study area, the results were not as neat as segmentation results in other locations. The presence of similar properties as water regions and background aspects might influence the segmentation results, which were then improved by performing post-processing steps. Besides, water stains on the lens during rainfall can also contribute to poor segmentation outcomes [80]. To visually examine the rising and falling of water levels, water level values were extracted from LiDAR data based on on-site references and the reference locations were identified in the surveillance image as well. This work proposed to use water level markers from LiDAR data as a threshold value reference to avoid error when estimating water levels using target objects that are present in the study area [81]. On-site data collection was undertaken during normal water conditions so that several water level thresholds could be adopted including the normal water level. Four water level thresholds namely normal level, alert level, warning level, and danger level were employed, to follow the standard water level thresholds used by DID. The results obtained illustrate the fluctuation of water levels in the study area during raining season. The relationship between water level estimation using the proposed framework and water level measurement from the sensor was investigated using [PERSON]'s rank-order correlation coefficient. Both variables proved to have a strong correlation. Therefore, the concept was applied to the study area that has no water level sensor available.