Drone-Based Water Level Detection in Flood Disasters

Abstract

Japan was hit by typhoon Hagibis, which came with torrential rains submerging almost eight-thousand buildings. For fast alleviation of and recovery from flood damage, a quick, broad, and accurate assessment of the damage situation is required. Image analysis provides a much more feasible alternative than on-site sensors due to their installation and maintenance costs. Nevertheless, most state-of-art research relies on only ground-level images that are inevitably limited in their field of vision. This paper presents a water level detection system based on aerial drone-based image recognition. The system applies the R-CNN learning model together with a novel labeling method on the reference objects, including houses and cars. The proposed system tackles the challenges of the limited and wild data set of flood images from the top view with data augmentation and transfer-learning overlaying Mask R-CNN for the object recognition model. Additionally, the VGG16 network is employed for water level detection purposes. We evaluated the proposed system on realistic images captured at disaster time. Preliminary results show that the system can achieve a detection accuracy of submerged objects of 73.42% with as low as only 21.43 cm error in estimating the water level.

Calibrated river-level estimation from river cameras using convolutional neural networks

“…A third solution that would consist in merging the camera images with digital elevation models can only be performed manually at this stage, as current literature suggests that deep learning methods are not accurate enough to perform such tasks (Mertan et al, 2021). An object detection approach has also been considered to evaluate flood situations in urban areas (Rizk et al, 2022). However, this methodology relies on the deployment of drones and the presence of specific objects (flooded cars or houses), which limits the potential of the method to capture the rising limb of the flood (before the deployment of the drones and/or before the cars and homes get flooded).…”

“…An object detection approach has also been considered to evaluate flood situations in urban areas (Rizk et al, 2022). However, this methodology relies on the deployment of drones and the presence of specific objects (flooded cars or houses), which limits the potential of the method to capture the rising limb of the flood (before the deployment of the drones and/or before the cars and homes get flooded).…”

Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network

“…Consequently, artificial intelligence techniques have been utilized in the provisioning of civilian and military services [2]. In this context, drones have been adopted for express shipping and delivery [3][4][5], natural disaster prevention [6,7], geographical mapping [8], search and rescue operations [9], aerial photography for journalism and film making [10], providing essential materials [11], border control surveillance [12], and building safety inspection [13]. Even though drone technology offers a multitude of benefits, it raises mixed concerns when it comes to how it will be used in the future.…”

Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery

“…In addition, they can also be used to quickly search for damage after a disaster has passed. The primary disadvantage of UAVs, however, is the smaller coverage area (a few square kilometers) due to the limitations of energy reserves, regulations on flying height and speed, and regulations to protect air traffic, safety of people and property on the ground, as well as privacy [30][31][32]. UAVs are also limited by the climate condition [30,[33][34][35].…”

“…Despite the promise of quick and cheap surveillance, there are still more limitations to overcome when using UAVs for water level measurement. First, because UAVs are relatively new, there is a lack of pre-existing data available for training the AI that will be used to identify water depth where data augmentation is generally needed for a wide variety of images resembling different conditions (lighting levels, image quality, and alternate viewing angles) [31]. In addition, because the newly generated images are already annotated, it is not necessary to spend time reviewing the dataset.…”