Seafloor Mapping Research Articles

Research and Applications of Autonomous Underwater Vehicles

Autonomous Underwater Vehicle (AUV) technology has been developed in the field of underwater surveying over the past decades. Today as climate change and the energy crisis becoming increasingly urgent matters. Simultaneously, as technology is moving towards data-driven digitization, AUV technology is expanding into several other fields. This paper provides an overview of the current development of AUVs. It discusses the strength of AUV technology, such as high survey accuracy, large operational range and low operational costs compared to other surveying methods. It also addresses limitations, including battery technology constrains and wireless communication challenges. This paper will also introduce future areas of development based on the limitations, such as fuel cell, underwater scanning and mapping, underwater autonomous navigation, and bio-mimicry technology. Finally, based on the currently technology developments of AUVs, discuss the currently fields of application such as oceanography, underwater mapping and imaging and future fields of applications such as fisheries and use in underwater industrial inspections.

Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review

Positioning systems are integral to Unmanned Underwater Vehicle (UUV) operation, enabling precise navigation and control in complex underwater environments. This paper comprehensively reviews the key technologies employed for UUV positioning, including acoustic systems, inertial navigation, Doppler velocity logs, and GPS when near the surface. These systems are essential for seabed mapping, marine infrastructure inspection, and search and rescue operations. The review highlights recent technological advancements and examines the integration of these systems to enhance accuracy and operational efficiency. It also addresses ongoing challenges, such as communication constraints, environmental variability, and discrepancies between theoretical models and field applications. Future trends in positioning system development are discussed, with a focus on improving reliability and performance in diverse underwater conditions to support the expanding capabilities of UUVs across scientific, commercial, and rescue missions.

Enhancing Marine Topography Mapping: A Geometrically Optimized Algorithm for Multibeam Echosounder Survey Efficiency and Accuracy

This research introduces a new multibeam survey line model that optimizes geometric relationships to improve the efficiency and accuracy of surveys over complex seabed topographies. Since existing multibeam echosounder systems have limitations in handling complex terrains, this study presents an advanced model to enhance data quality and operational efficiency. By strategically designing survey lines and optimizing coverage strategies, this paper achieves the optimal configuration of survey lines for secondary measurements in marine areas, ensuring high precision and reliability of measurement data. Experimental results show that the new model significantly outperforms existing technologies in terms of effective coverage area and measurement accuracy, with an average coverage rate of over 95%, higher than existing models, and a weighted average overlap rate of 3.18%, greatly improving the economic efficiency of measurements by reducing redundant coverage and minimizing operational costs. These findings confirm the advantages of the new model in practical applications and offer valuable technical support for advancing seabed mapping technology.

Visual seafloor mapping with autonomous robots.

Autonomous robots adopt navigation-aided hierarchical reconstruction to visually map the seafloor.

POSIT: An automated tool for detecting and characterizing diverse morphological features in raster data - Application to pockmarks, mounds, and craters

Accurate detection and characterization of seafloor morphologies are crucial for marine researchers and industries involved in underwater mapping, environmental monitoring, or resource exploration. Although their detection has relied on visual inspection of detailed bathymetries, few efforts to automate the process can be found in the literature. This study presents a novel MatLab computer code called POSIT (Feature Signature Detection) based on the convolution and correlation with a structural element containing the shape to search for. POSIT is successfully tested on both synthetic and real datasets, encompassing marine and terrestrial digital elevation models of different resolution and on a digital image. The centroids of submarine pockmarks and mounds, terrestrial volcanic craters and lunar craters are calculated with zero dispersion and perfect location, and their geometric parameters and confidence are provided.

Refined Bathymetric Prediction Based on Feature Extraction of Gravity Field Signals: BATHY‐FE

AbstractThe resolution of altimetry‐derived gravity signals renders traditional bathymetry inversion methods inadequate for detecting short‐wavelength bathymetric features. This study presents a new global seafloor topography model by merging regional models constructed via a deep learning approach recently developed by the authors. Feature extraction techniques were used to refine each regional bathymetric model, resulting in sharpened features that are fuzzily revealed (or not seen) in global bathymetric models such as GEBCO_2023 and SRTM15+V2, especially in areas close to the poles. This is proof that, with regard to current gravity field products, conventional bathymetry inversion methods are weaker at generalizing to uncharted locations of the global ocean floor. At test points, the global seafloor model has mean error and error standard deviation of 1.75 ± 81.15 m. The refined seafloor topography can be used to supplement existing seafloor maps, especially in the polar regions. It is useful for studying seafloor geography, and also for studies in which seafloor ruggedness is required.

Non-invasive investigation of a submerged medieval harbour, a case study from Puck Lagoon

This study presents an innovative approach to underwater archaeological prospection using non-invasive methods of seabed exploration. The research focuses on the Puck medieval harbour, a cultural heritage site, and utilises acoustic and optical underwater remote-sensing technology. The primary objectives include optimising the use of Airborne Laser Bathymetry in underwater archaeology, enhancing the filtration process for mapping underwater sites, and utilising data from both multibeam echosounder and bathymetric LiDAR for detailed archaeological research. A systematic approach to data processing is advocated to maximise the value of collected data and inform subsequent investigations. The study demonstrates the high accuracy of bathymetric datasets from optical and acoustic sources, with an R-squared correlation coefficient of 0.9853. The application of these techniques is demonstrated, underscoring the compatibility and effective combination of these methods in underwater archaeological exploration. This research provides valuable insights and directions for future archaeological prospections based on active remote-sensing measurements.

Underwater Mapping and Optimization Based on Multibeam Echo Sounders

Multibeam echo sounders (MBESs) enable extensive underwater environment exploration. However, due to weak correlation between adjacent multibeam sonar data and difficulties in inter-frame feature matching, the resulting underwater mapping accuracy frequently falls short of the desired level. To address this issue, this study presents the development of a multibeam data processing system, which includes functionalities for sonar parameter configuration, data storage, and point cloud conversion. Subsequently, an Iterative Extended Kalman Filter (iEKF) algorithm is employed for odometry estimation, facilitating the initial construction of the point cloud map. To further enhance mapping accuracy, we utilize the Generalized Iterative Closest Point (GICP) algorithm for point cloud registration, effectively merging point cloud data collected at different times from the same location. Finally, real-world lake experiments demonstrate that our method achieves an Absolute Trajectory Error (ATE) of 15.10 m and an average local point cloud registration error of 0.97 m. Furthermore, we conduct measurements on various types of artificial targets. The experimental results indicate that the average location error of the targets calculated by our method is 4.62 m, which meets the accuracy requirements for underwater target exploration.

Application and Evaluation of the AI-Powered Segment Anything Model (SAM) in Seafloor Mapping: A Case Study from Puck Lagoon, Poland

Application and Evaluation of the AI-Powered Segment Anything Model (SAM) in Seafloor Mapping: A Case Study from Puck Lagoon, Poland

Mapping Seafloor Sediment Distributions Using Public Geospatial Data and Machine Learning to Support Regional Offshore Renewable Energy Development

Mapping Seafloor Sediment Distributions Using Public Geospatial Data and Machine Learning to Support Regional Offshore Renewable Energy Development

Semihierarchical reconstruction and weak‐area revisiting for robotic visual seafloor mapping

AbstractDespite impressive results achieved by many on‐land visual mapping algorithms in the recent decades, transferring these methods from land to the deep sea remains a challenge due to harsh environmental conditions. Images captured by autonomous underwater vehicles, equipped with high‐resolution cameras and artificial illumination systems, often suffer from heterogeneous illumination and quality degradation caused by attenuation and scattering, on top of refraction of light rays. These challenges often result in the failure of on‐land Simultaneous Localization and Mapping (SLAM) approaches when applied underwater or cause Structure‐from‐Motion (SfM) approaches to exhibit drifting or omit challenging images. Consequently, this leads to gaps, jumps, or weakly reconstructed areas. In this work, we present a navigation‐aided hierarchical reconstruction approach to facilitate the automated robotic three‐dimensional reconstruction of hectares of seafloor. Our hierarchical approach combines the advantages of SLAM and global SfM that are much more efficient than incremental SfM, while ensuring the completeness and consistency of the global map. This is achieved through identifying and revisiting problematic or weakly reconstructed areas, avoiding to omit images and making better use of limited dive time. The proposed system has been extensively tested and evaluated during several research cruises, demonstrating its robustness and practicality in real‐world conditions.

Integrating Seabed Topography and Ocean Current Dynamics for Submarine Trajectory Analysis

In the face of growing global ocean exploration, the research focuses on overcoming the challenges of deep-sea navigation and the unpredictability of ocean currents to improve submarine pathway planning and trajectory prediction. This study proposes a method for improving submarine navigation and trajectory prediction through digital seabed mapping of the Ionian Sea and ocean current simulation. Using NCEI data, it transforms latitude and longitude into a geographic model, bypassing seawater variables like salinity and temperature. The approach combines sine functions and Gaussian noise to mimic ocean currents' natural variability, enhancing position predictions. Simplifying the submarine as a sphere, it integrates ocean currents into motion analysis and employs MATLAB for simulation, including scenarios of contact loss and breakup. Recognizing model limitations, it recommends using sensors on submarines for real-time data collection, aiming to refine navigational accuracy amidst environmental uncertainties.

Application of Photogrammetric Computer Vision and Deep Learning in High-Resolution Underwater Mapping: A Case Study of Shallow-Water Coral Reefs

Abstract. Underwater mapping is vital for engineering applications and scientific research in ocean environments, with coral reefs being a primary focus. Unlike more uniform and predictable terrestrial environments, coral reefs present a unique challenge for 3D reconstruction due to their intricate and irregular structures. Traditional 3D reconstruction methods struggle to accurately capture the nuances of coral reefs. This is primarily because coral reefs exhibit a high degree of spatial heterogeneity, featuring diverse shapes, sizes, and textures. Additionally, the dynamic nature of underwater conditions, such as varying light, water clarity, and movement, further complicates the accurate geometrical estimation of these ecosystems. With the rapid advancement of photogrammetric computer vision and deep learning technologies, there are emerging methods that have potential to enhance the quality of its 3D reconstruction. In this context, this study formulates a coral reef reconstruction workflow that incorporates these cutting-edge technologies. This workflow is divided into two core stages: sparse reconstruction and dense reconstruction. We conduct individual summaries of the relevant research efforts in these stages and outline the available methods. To assess the specific capabilities of these methods, we apply them to real-world coral reef images and conduct a comprehensive evaluation. Additionally, we analyze the strengths and weaknesses of different methods and identify areas for improvement. We believe this study offers valuable references for future research in underwater mapping.

Underwater Camera Calibration Based on Double Refraction

Underwater camera calibration plays a pivotal role in underwater positioning and underwater mapping reconstruction, making it crucial for achieving precise spatial measurements in underwater environments. To elevate measurement accuracy, we have refined the calibration methodology for underwater cameras. Firstly, we conducted an in-depth investigation into the intricate challenges posed by double refraction errors arising from light passing through water–glass–air interfaces. To address this issue, we established a double refraction model based on the actual underwater light paths, laying the groundwork for our calibration efforts. Furthermore, to tackle the problem of determining multiple camera parameters, we proposed a parameter optimization method based on genetic algorithms, capable of navigating the complex parameter space. Finally, compared with other algorithms, our method enables more precise determination of underwater camera parameters.

Feasibility of Using Green Laser for Underwater Infrastructure Monitoring: Case Studies in South Florida

Scour around bridges present a severe threat to the stability of railroad and highway bridges. Scour needs to be monitored to prevent the bridges from becoming damaged. This research studies the feasibility of using green laser for monitoring the scour around candidate railroad and highway bridges. The laboratory experiments that provided the basis for using green laser for underwater mapping are also discussed. The results of the laboratory and field experiments demonstrate the feasibility of using green laser for underwater infrastructure monitoring with limitations on the turbidity of water that affects the penetrability of the laser. This method can be used for scour monitoring around offshore structures in shallow water as well as corrosion monitoring of bridges.

Multitemporal Seagrass Mapping and Monitoring of Posidonia Meadows and Banquettes for Blue Carbon Conservation: The Poseidon Project

Abstract. In the framework of mapping underwater environments, Posidonia Oceanica (PO) is a fundamental indicator for environmental monitoring and related studies and analyses for climate change. The POSEIDON project aims to study, develop and test new methodologies to provide knowledge, as well as ultra-high-resolution (UHR) and beyond ultra-high-resolution (BUHR) mapping and monitoring data for the conservation of the PO and related meadows and banquettes in the Mediterranean area. The project is funded by the National Recovery and Resilience Plan and stems from the recommendation of international organizations (European Environmental Directives, Mediterranean PO Network, International Partnership for Blue Carbon, Intergovernmental Oceanographic Commission of UNESCO in the Save the Wave project). Starting from new integrated survey methods and legacy data, the PO, ichthyofauna and seagrass banquettes will be mapped to understand their evolution and extract indicators and useful trends. Furthermore, the produced information will be stored and shared in a webGIS and a dedicated app to prevent unexpected damages during sailing or other activities. POSEIDON will use imaging sensors (RGB and multi/hyperspectral cameras) to extract information. With specific lighting equipment for significant water depths, these sensors will be conducted (by scuba divers or Autonomous Underwater Vehicles) next to the object to acquire BUHR data. Finally, to monitor the fauna (fish, other species, invertebrates), terrestrial and underwater videogrammetry will be applied using mono and stereo-cameras with object detection algorithms.

Geohazard features of the Tyrrhenian Calabria

ABSTRACT This paper accompanies the Maps of Geohazard features of the Cilento and the Calabro-Tyrrhenian continental margin in the southern Tyrrhenian Sea (Italy). The main geohazard-related features were derived from extensive seafloor mapping through the collection of high-resolution multibeam data acquired during several oceanographic cruises. They encompass many fluids seepage features, fault scarps, landslides scars, gullies, channels, and canyons. Hazards related to coastal landslides and shelf-indenting canyons are very high in these sectors (especially in southern Calabria) due to active seismicity coupled with rapid uplift, high sedimentation rates and narrow or totally absent continental shelf, thus promoting a direct connection between steep slopes and coastal areas. In this setting, mass-wasting features can directly impact coastal or submarine infrastructures or indirectly create local tsunami waves, as observed in historical times. Moreover, this physiographic setting of the margin facilitates the transfer of marine litter toward deep-sea areas.

Multivariate mapping of seabed grain size parameters in the Bay of Fundy using convolutional neural networks

High-resolution seabed sediment information is critical for a range of marine spatial planning applications in multi-use shelf environments. To establish this information for the Bay of Fundy, Canada, legacy seabed sediment measurements were obtained from regional data compilations, and eight parameters describing the grain size were modelled across the extent of the bay using high resolution acoustic seafloor mapping and oceanographic datasets. This was achieved using a purpose-made convolutional neural network configured for geospatial modelling of multivariate grain size parameters. Shared information between the response parameters enabled model training with partially complete observations from the varied legacy data sources, and an explicit multiscale model architecture ensured that environmental predictors were implemented at appropriate scales for modelling each parameter. This avoids typical exhaustive exploration and selection of scale-specific predictor sets that often precede model building. Compositional grain size parameters were additionally accommodated using appropriate output activation functions, providing an efficient alternative to compositional data transformation and imputation. Results agreed well with our current understanding of the surficial geology of the bay, and cross-validation was used to quantitatively evaluate map predictions. Of the eight predicted parameters, the mean grain size and mud (clay and silt) fractions were predicted with high accuracy (> 50% variance explained); the accuracy of grain size skewness was comparatively low (24% variance explained). Exploration of variable importance suggested that compiled acoustic backscatter was the most important environmental variable for predicting the grain size, but that geographic information describing the latitude and longitude within the bay was also highly useful. We hypothesize an interaction between these variables that enables location-specific prediction. Data layers of predicted grain size parameter values are made available for further sedimentological and ecological exploration, and for marine spatial planning activities within the bay.

Closing the data gap – Automated seafloor health maps to accelerate nature-based solutions

This article delves into the pivotal role of the oceans, specifically the seafloor, in addressing climate change and food security. Despite its significance, a lack of detailed data on the seafloor hampers informed decision-making by policymakers, impedes sustainable blue economy investments, and hinders the development of blue carbon and biodiversity credit markets. PlanBlue, an innovative technology company, has devised a groundbreaking solution using advanced imaging, underwater navigation, and machine learning to automate seafloor mapping. This article explores the importance of seafloor data, the barriers to access data, and how PlanBlue's technology can improve our understanding of the ocean's potential. The discussion encompasses the significant role of the seafloor as a carbon sink, the necessity for credible data in ocean conservation, and the crucial link between seafloor mapping and effective marine spatial planning. PlanBlue's innovative approach not only adds speed and scale to data collection but also provides multi-layered insights into species health, biomass, and more, contributing to the credibility of marine conservation efforts. The article concludes with a scientific review highlighting the accuracy and meaningfulness of PlanBlue's methodology, validating its transformative impact on the blue carbon industry and seafloor habitat monitoring.

An Underwater Localization Method Based on Visual SLAM for the Near-Bottom Environment

The feature matching of the near-bottom visual SLAM is influenced by underwater raised sediments, resulting in tracking loss. In this paper, the novel visual SLAM system is proposed in the underwater raised sediments environment. The underwater images are firstly classified based on the color recognition method by adding the weights of pixel location to reduce the interference of similar colors on the seabed. The improved adaptive median filter method is proposed to filter the classified images by using the mean value of the filter window border as the discriminant condition to retain the original features of the image. The filtered images are finally processed by the tracking module to obtain the trajectory of underwater vehicles and the seafloor maps. The datasets of seamount areas captured in the western Pacific Ocean are processed by the improved visual SLAM system. The keyframes, mapping points, and feature point matching pairs extracted from the improved visual SLAM system are improved by 5.2%, 11.2%, and 4.5% compared with that of the ORB-SLAM3 system, respectively. The improved visual SLAM system has the advantage of robustness to dynamic disturbances, which is of practical application in underwater vehicles operated in near-bottom areas such as seamounts and nodules.