Grzegorz Zaniewicz

Time and accuracy are key elements of underwater search and rescue operations. This article proposes a system with a modified U-net architecture, leveraging spatial and channel attention techniques for binary segmentation of drowned victims from side-scan sonar imagery. The base architecture is enhanced by a novel spatial skip-connection attention (SSCA) and multihead attention (MHA). The SSCA improves feature representation by highlighting relevant spatial information in the shallow layers. At the same time, the MHA module captures abstract channel-wise relations, improving the information flow and feature representation deeper in the network. In our experiments, we used original samples collected from the Oder River in Szczecin, using a drowned dummy and an Edgetech 4125 for sonar imaging, to produce a novel sonar drowned victims data set. The results obtained on the test set (99.54% accuracy and 75.76% mean dice coefficient) show that the model can adapt to the task, achieving significant results.

Sonar imaging provides representative real-time mapping of the water bottom, which is particularly valuable in search-and-recovery operations, as it helps quickly locate drowned victims. However, practical analysis of sonar data is often challenging due to its complexity and automated recognition demanding substantial training data. In this paper, we present an extension to SDVD (Sonar Drowned Victims Dataset), a sonar image dataset with annotated binary masks marking drowned victims for training segmentation models. To improve segmentation processing, we propose Multi-Channel Input Augmentation (MCIA). This method augments the segmented image with additional channels, increasing it from one to seven, increasing the model’s information input. To achieve this, each channel represents a processed original image by applying: Sobel edge detection, Haar wavelet, High-Pass features, pixelated map, CLAHE and distance transform. To evaluate segmentation performance, we tested different models on all SDVD subsets, alongside various data augmentation techniques, such as RICAP. Among the models, MCIA-U2Net demonstrated the highest accuracy, achieving an Intersection over Union (IoU) of 75.89% and a Dice Score of 86.30% on base SDVD, 70.12% IoU and 82.44% Dice Score on CleanedSDVD, 68.55% IoU and 81.33% Dice

Analysis of the river bottom using side-scan sonar allows for obtaining the so-called "waterfall" image - a scrolling visualization of sonar reflections from the bottom and underwater objects. This type of image allows for a detailed characterization of the bottom structure and detection of various objects. This image is created based on the measurement of the return time of the sound wave reflected from various surfaces and the position of the sonar. Unfortunately, quite often, objects (such as bridges) contribute to the loss of GPS signals and, consequently, the generation of distortions in this area. In this paper, we present a recurrent neural network model with attention modules to predict the lost GPS signal during measurements. The proposed predictor architecture combines local processing with a deep tower of alternating unidirectional and bidirectional LSTM (Long Short-Term Memory) layers with residual connections. Furthermore, we implemented multihead self-attention to capture global temporal dependencies and RevIN (Reversible Instance Normalization), which allows for adaptation to changing input data distributions. The evaluation of the methodology was tested on data collected in real conditions on the Odra River in Szczecin, Poland. The results indicate a very good adaptation of the predictor to GPS signal correction during its loss under bridges.

Digital twin is an attractive technology for the representation of objects due to its ability to produce precise measurements and their geovisualisation. Of special interest is the application and fusion of various remote sensing techniques for shallow river and inland water areas, commonly measured using conventional surveying or multimodal photogrammetry. The construction of spatial digital twins of river areas requires the use of multi-platform and multi-sensor measurements to obtain reliable data of the river environment. Due to the high dynamics of river changes, the cost of measurements and the difficult-to-access measurement area, the mapping should be large-scale and simultaneous. To address these challenges, the authors performed an experiment using three measurement platforms (boat, plane, UAV) and multiple sensors to acquire both cloud and image spatial data, which were integrated temporally and spatially. The integration methods improved the accuracy of the resulting digital model by approximately 20 percent.

This study is structured to address the problem of mapping the bottom of shallow and ultra-shallow inland water bodies using high-resolution satellite imagery. These environments, with their diverse distribution of optically relevant components, pose a challenge to traditional mapping methods. The study was conducted on several research issues, each focusing on a specific aspect of the SDB, related to the selection of spectral bands and regression models, regression models creation, evaluation of the influence of the number and spatial distribution of reference soundings, and assessment of the quality of the bathymetric surface, with a focus on microtopography. The study utilized basic empirical techniques, incorporating high-precision reference data acquired via an unmanned surface vessel (USV) integrated with a single-beam echosounder (SBES), and Global Navigation Satellite System (GNSS) receiver measurements. The performed investigation allowed the optimization of a methodology for bathymetry acquisition of such areas by identifying the impact of individual processing components. The first results indicated the usefulness of the proposed approach, which can be confirmed by the values of the obtained RMS errors of elaborated bathymetric surfaces in the range of up to several centimeters in some study cases. The work also points to the problematic nature of this type of study, which can contribute to further research into the application of remote sensing techniques for bathymetry, especially during acquisition in optically complex waters.

The aim of the research is to identify the optimal method for smoothing the surface of a hybrid digital bathymetric model (HDBM). The initiation of this research is justified by the fact that a model created from diverse types of data may have different surface textures and outliers. This diversity may cause problems in subsequent data processing stages, such as generating depth contours. As part of the adopted research methodology, fifteen filters were analysed. Filtering techniques were examined for filter type, the number of iterations, weights, and window size. The result is the adopted research methodology, which enabled the selection of the optimal filtering method. The research undertaken in this work is an extension of the methodology for developing an HDBM. An important aspect of the research is the approach to elaborating on such kinds of models in shallow and ultra-shallow waters adjacent to the land, as well as the use of data obtained by modern measurement platforms, such as unmanned surface vehicles (USV) and unmanned aerial vehicles (UAV). The studies fit into the general context of works related to the development of this type of model and undoubtedly provide a solid reference for further development or improvement of similar methods.

This paper presents a method for integrating data acquired by unmanned surface vehicles and unmanned aerial vehicles. The aim of this work was to create a uniform bathymetric surface extending to the shoreline. Such a body of water is usually characterized by ultra-shallow depths, which makes measurement impossible even with hydrographic autonomous vessels. Bathymetric data acquired by the photogrammetric method are, however, characterized by large errors with increasing depth. The presented method is based on processing of two data sets using a bathymetric reference surface and selection of points on the basis of generated masks. Numerical bathymetric models created by interpolation methods confirmed the usefulness of the concept adopted.

The research in this paper is concerned with the development of a continuous elevation model in the coastal zones of inland waters. The source data for the creation of numerical terrain models were data acquired by contemporary sensors, such as such as a single-beam echosounder and an unmanned aircraft system. Different interpolation methods were tested in the study. A new approach in the research field is an interpolation method based on the processing of datasets with different degrees of spatial data reduction. The authors call it the Spatial Interpolation Method based on Data Reduction (SIMDR). The choice of method is based on quantitative and qualitative analysis, taking into account the type of interpolation and the method of geodata reduction. A proposal for the practical implementation of the method involves script processing, which automates the processes of modeling and error calculation.

Target tracking is a process that provides information about targets in a specific area and is one of the key issues affecting the safety of any vehicle navigating in water. The main sensor used for underwater target tracking is sonar, with one of the most popular configurations being forward looking sonar (FLS). The target tracking state vector is usually estimated with the use of numerical filter algorithms, such as the Kalman filter (KF) and its modification, or the particle filter (PF). This requires the definition of a process model, including process noise, and a measurement model. This study focused on process noise definition. It is usually implemented as Gaussian noise, with a covariance matrix defined by the author. An analytical and empirical analysis was conducted, including a verification of the existing approaches and a survey of the published literature. Additionally, a theoretical analysis of the factors influencing process noise was conducted, which was followed by an empirical verification. The results were discussed, leading to the conclusions. The results of the theoretical analysis were confirmed by the empirical experiment and the results were compared with commonly used values of process noise in underwater target tracking processes.

This article presents possible applications of a dynamic gravity meter (MGS-6, Micro-g LaCoste) for determining the dynamic height along the Odra River, in northwest Poland. The gravity measurement campaign described in this article was conducted on a small, hybrid-powered survey vessel (overall length: 9.5 m). We discuss a method for processing the results of gravimetric measurements performed on a mobile platform affected by strong external disturbances. Because measurement noise in most cases consists of signals caused by non-ideal observation conditions, careful attempts were made to analyze and eliminate the noise. Two different data processing strategies were implemented, one for a 20 Hz gravity data stream and another for a 1 Hz data stream. A comparison of the achieved results is presented. A height reference level, consistent for the entire estuary, is critical for the construction of a safe waterway system, including 3D navigation with the dynamic estimation of under-keel clearance on the Odra and other Polish rivers. The campaign was conducted in an area where the accuracy of measurements (levelling and gravimetric) is of key importance for shipping safety. The shores in the presented area of interest are swampy, so watercraft-based measurements are preferred. The method described in the article can be successfully applied to measurements in all near-zero-depth areas.