Towards automatic real-time water level estimation using surveillance cameras

Abstract

<p>The handling of natural disasters, especially heavy rainfall and corresponding floods, requires special demands on emergency services. The need to obtain a quick, efficient and real-time estimation of the water level is critical for monitoring a flood event. This is a challenging task and usually requires specially prepared river sections. In addition, in heavy flood events, some classical observation methods may be compromised.</p><p>With the technological advances derived from image-based observation methods and segmentation algorithms based on neural networks (NN), it is possible to generate real-time, low-cost monitoring systems. This new approach makes it possible to densify the observation network, improving flood warning and management. In addition, images can be obtained by remotely positioned cameras, preventing data loss during a major event.</p><p>The workflow we have developed for real-time monitoring consists of the integration of 3 different techniques. The first step consists of a topographic survey using Structure from Motion (SfM) strategies. In this stage, images of the area of interest are obtained using both terrestrial cameras and UAV images. The survey is completed by obtaining ground control point coordinates with multi-band GNSS equipment. The result is a 3D SfM model georeferenced to centimetre accuracy that allows us to reconstruct not only the river environment but also the riverbed.</p><p>The second step consists of segmenting the images obtained with a surveillance camera installed ad hoc to monitor the river. This segmentation is achieved with the use of convolutional neural networks (CNN). The aim is to automatically segment the time-lapse images obtained every 15 minutes. We have carried out this research by testing different CNN to choose the most suitable structure for river segmentation, adapted to each study area and at each time of the day (day and night).</p><p>The third step is based on the integration between the automatically segmented images and the 3D model acquired. The CNN-segmented river boundary is projected into the 3D SfM model to obtain a metric result of the water level based on the point of the 3D model closest to the image ray.</p><p>The possibility of automating the segmentation and reprojection in the 3D model will allow the generation of a robust centimetre-accurate workflow, capable of estimating the water level in near real time both day and night. This strategy represents the basis for a better understanding of river flooding and for the development of early warning systems.</p>

A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery

“…validate numerical models or to acquire accurate data for early warning systems, the segmentation of river water in close-range Remote Sensing (RS) images captured by lowcost sensors becomes particularly significant [3], [4]. Indeed, close-range RS images captured by low-cost cameras (e.g., smartphone/surveillance cameras) are proven to facilitate the detection of subtle variations in river water properties and the surrounding terrain [3], [5], [6]. This presents, until yet, a rarely utilized and systematically investigated opportunity to extract nuanced insights into hydrological parameters or any related process (e.g., water level, water turbidity, floating debris, etc.)…”

“…This presents, until yet, a rarely utilized and systematically investigated opportunity to extract nuanced insights into hydrological parameters or any related process (e.g., water level, water turbidity, floating debris, etc.) from close-range RS images [3], [6].…”

“…To enhance real-time water level monitoring and address these limitations, Eltner et al [30] introduced UPerNet [26] with the ResNeXt-50 backbone as a well-generalized CNN model, chosen from a range of DL models for water segmentation in various geographical contexts. Furthermore, Wagner et al [3] conducted a widespread study evaluating 32 DL segmentation models by introducing high-quality RIWA.v1 [6], [31] for water detection with online/offline augmentation.…”

“…A more comprehensive analysis across multiple datasets with diverse conditions is essential to better assess the capabilities and generalizability of each model. While the RIWA dataset [31] provides valuable support for this research area, there remains a critical need for additional accurately labeled datasets to further enhance model training and evaluation.…”

“…We collected close-range images from rivers using various platforms, such as smartphones, cameras, surveillance cameras, and low-altitude drones, to introduce a new dataset named LuFI-RiverSnap.v1. It comprises over 800 images with precise annotations, along with a few suitable images from the Kaggle WaterNet [36], Elbersdorf / Wesenitz [37], RIWA.v1 [31] datasets. To support and advance the development of river water segmentation tasks, we have released the recently introduced LuFI-RiverSnapv1 dataset at (Dataset link).…”