Search Results (146)

Archaeological research fundamentally relies on detecting features to uncover hidden historical information. Airborne (aerial) LiDAR technology has significantly advanced this field by providing high-resolution 3D terrain maps that enable the identification of ancient structures and landscapes with improved accuracy and efficiency. This technical note comprehensively reviews 45 recent studies to critically examine the integration of Machine Learning (ML) and Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs), with airborne LiDAR derivatives for automated archaeological feature detection. The review highlights the transformative potential of these approaches, revealing their capability to automate feature detection and classification, thus enhancing efficiency and accuracy in archaeological research. CNN-based methods, employed in 32 of the reviewed studies, consistently demonstrate high accuracy across diverse archaeological features. For example, ancient city walls were delineated with 94.12% precision using U-Net, Maya settlements with 95% accuracy using VGG-19, and with an IoU of around 80% using YOLOv8, and shipwrecks with a 92% F1-score using YOLOv3 aided by transfer learning. Furthermore, traditional ML techniques like random forest proved effective in tasks such as identifying burial mounds with 96% accuracy and ancient canals. Despite these significant advancements, the application of ML/DL in archaeology faces critical challenges, including the scarcity of large, labeled archaeological datasets, the prevalence of false positives due to morphological similarities with natural or modern features, and the lack of standardized evaluation metrics across studies. This note underscores the transformative potential of LiDAR and ML/DL integration and emphasizes the crucial need for continued interdisciplinary collaboration to address these limitations and advance the preservation of cultural heritage. Full article

This study provides an up-to-date assessment of the environmental status in the area of the S/s Stuttgart wreck in the southern Baltic Sea, focusing on macrozoobenthos, sediment chemistry, and contamination in Mytilus trossulus soft tissues. Comparative analyses from 2016 and 2023 revealed increased species richness and distinct benthic assemblages, shaped primarily by depth and distance from the wreck. Among macrozoobenthos, there dominated opportunistic species, characterized by a high degree of resistance to the unfavorable state of the environment, suggesting adaptation to local conditions. Elevated concentrations of heavy metals were detected in sediments, with maximum values of Cd—0.85 mg·kg⁻¹, Cu—34 mg·kg⁻¹, Zn—119 mg·kg⁻¹, and Ni—32.3 mg·kg⁻¹. However, no significant correlations between sediment contamination and macrozoobenthos composition were found. In Mytilus trossulus, contaminant levels were mostly within regulatory limits; however, mercury concentrations reached 0.069 mg·kg⁻¹ wet weight near the wreck and 0.493 mg·kg⁻¹ at the reference station, both exceeding the threshold defined in national legislation (0.02 mg·kg⁻¹) (Journal of Laws of 2021, item 568). Condition indices for Macoma balthica were lower in the wreck area, suggesting sublethal stress. Ecotoxicological tests showed no acute toxicity in most sediment samples, emphasizing the complexity of pollutant effects. The data presented here not only enrich the existing literature on marine pollution but also contribute to the development of more effective environmental protection strategies for marine ecosystems under international protection. Full article

This article focuses on the story of a pirogue shipwreck that occurred in early September 2024, less than two miles from the coast of Mbour, about 90 km south of Dakar. It traces an ethnographic account of that tragic event through the lenses of different voices, standpoints, and testimonies from the survivors, the relatives and friends of the victims, and those involved in the organization of both the aborted ocean crossing and the rescue operations in various ways. By situating this extreme story of “potential migrants” among other accounts of migrants who disappeared at sea and of missing pirogues, the focus shifts to the different weights and possibilities of movement when dealing with disappearance and death, the unknown and known facts, addressing that which remains unknown even within this unambiguous and tragic event. Faced with the dense plot of ties at the core of that failed escape, we suggest that the reasons for the shipwreck are excess demand and solidarity, in terms of the impossibility of denying passage onboard the boat to friends, relatives, and neighbors. “On est ensemble” is therefore a way to recognize that there is no clear distinction or distance between captain and passengers, survivors and the dead, or victims and spectators, since in Mbour, everyone perfectly understands both the reasons and the risks, and the reason for the risks, of any illegal attempt to cross sea and land borders towards Europe. Full article

This study investigates the spatiotemporal evolution of oil released during the Agia Zoni II shipwreck in the Saronic Gulf in 2017, employing the OpenOil module of the OpenDrift framework. The simulation integrates oceanographic and meteorological data to model the transport, weathering, and fate of spilled oil over a six-day period. Oil behavior is examined across key transformation processes, including dispersion, emulsification, evaporation, and biodegradation, using particle-based modeling and a comprehensive set of environmental inputs. The modeled results are validated against in situ observations and visual inspection data, focusing on four critical dates. The study demonstrates OpenOil’s potential for accurately simulating oil dispersion dynamics in semi-enclosed marine environments and highlights the significance of environmental forcing, vertical mixing, and shoreline interactions in determining oil fate. It concludes with recommendations for improving real-time response strategies in similar spill scenarios. Full article

The removal of iron deposits on shipwreck surfaces by mechanical cleaning is labour-intensive work. This study develops an in situ gel and peeling cleaning method, utilising a carboxymethyl chitosan/tannic acid (CMCS/TA) colloidal solution spray on the surface of ferruginous deposits, promoting their removal by adhesion, chelation, and electrostatic bonding processes. The investigation confirmed that the CMTA-2 sample exhibited a sprayable viscosity of 263 mPa/s, the largest single removal thickness of 1.01 mm, a significant reduction in the fe/s atomic ratio by 2.53 units, and enhanced the deposit removal homogeneity. The field testing of the Nanhai I cultural relic showed a 14.37% reduction in iron concentration and a significant decrease in red colour (Δa* = 4.36). The synergistic mechanism involves TA chelating Fe²⁺/Fe³⁺ ions, while the CMCS gel network facilitates interfacial adhesion and mechanical peeling, hence promoting efficient and controllable cleaning. Full article

For two centuries, metallic shipwrecks have been relics of the history of navigation, trade, and wars. They are also hotspots of marine biodiversity. The degradation of these shipwrecks not only threatens their environment through the release of polluting compounds, but also the reef ecosystems that have developed. Microorganisms are at the root of both degradation and reef-building, and their roles are still more hypothetical than validated. The aim of this review is to focus on the known or suggested relationships between bacteria and metallic shipwrecks and to identify issues that highlight the need for multidisciplinary studies to better understand the mechanisms at play in these ecosystems with the aim of protecting both the environment and these sites of underwater cultural and natural heritage. Full article

Side-Scan Sonar (SSS) is widely used in underwater rescue operations and the detection of seabed targets, such as shipwrecks, drowning victims, and aircraft. However, the quality of sonar images is often degraded by noise sources like reverberation and speckle noise, which complicate the extraction of effective features. Additionally, challenges such as limited sample sizes and class imbalances are prevalent in side-scan sonar image data. These issues directly impact the accuracy of deep learning-based target classification models for SSS images. To address these challenges, we propose a side-scan sonar image classification model based on joint image deblurring–denoising and a pre-trained feature fusion attention network. Firstly, by employing transform domain filtering in conjunction with upsampling and downsampling techniques, the joint image deblurring–denoising approach effectively reduces image noise while preserving and enhancing edge and texture features. Secondly, a feature fusion attention network based on transfer learning is employed for image classification. Through the transfer learning approach, a feature extractor based on depthwise separable convolutions and densely connected networks is trained to effectively address the challenge of limited training samples. Subsequently, a dual-path feature fusion strategy is utilized to leverage the complementary strengths of different feature extraction networks. Furthermore, by incorporating channel attention and spatial attention mechanisms, key feature channels and regions are adaptively emphasized, thereby enhancing the accuracy and robustness of image classification. Finally, the Gradient-weighted Class Activation Mapping (Grad-CAM) technique is integrated into the proposed model to ensure interpretability and transparency. Experimental results show that our model achieves a classification accuracy of 96.80% on a side-scan sonar image dataset, confirming the effectiveness of this method for SSS image classification. Full article

The colonisation of the Americas and the discovery of its rich ores had a great impact on the world economies, making them quickly become the main suppliers of precious metals in Europe. The compositional studies of several coins (ducatons, eight reales cob8, four reales cob4, eight reales pillar dollar, four reales half pillar dollars, rijderschellings and silver rijders) recovered from the 18th-century Dutch East India Company Rooswijk wreck by micro X-ray fluorescence (µXRF) spectroscopy revealed further knowledge about the silver trade and the silver sources used to produce coins in mints in the Low Countries over a wide timeframe (1618–1739). The results provided trace elemental ‘fingerprints’ of coins minted with silver from known mines, and matching against them revealed the silver sources used in coins, whose mint location could not be identified due to their poor state of preservation. This study proved that, despite the decrease in silver production in European mines in the 17th century and the huge influx of American silver into Europe, in the 18th century, the mints in the Dutch Republic and, to a lesser extent, in the Spanish Netherlands still highly relied on the recycling of older coins and on the import of silver from central European mines. Full article

The characteristics of multi-directional rotation and high aspect ratio of targets such as shipwrecks lead to low detection accuracy and difficulty localizing existing detection models for this target type. Through our research, we design three main inconsistencies in rotating target detection compared to traditional target detection, i.e., inconsistency between target and anchor frame, inconsistency between classification features and regression features, and inconsistency between rotating frame quality and label assignment strategy. In this paper, to address the discrepancies in the above three aspects, we propose the Side-scan Sonar Dynamic Rotating Target Detector (S³DR-Det), which is a model with a dynamic rotational convolution (DRC) module designed to effectively gather rotating targets’ high-quality features during the model’s feature extraction phase, a feature decoupling module (FDM) designed to distinguish between the various features needed for regression and classification in the detection phase, and a dynamic label assignment strategy based on spatial matching prior information (S-A) specific to rotating targets in the training phase, which can more reasonably and accurately classify positive and negative samples. The three modules not only solve the problems unique to each stage but are also highly coupled to solve the difficulties of target detection caused by the multi-direction and high aspect ratio of the target in the side-scan sonar image. Our model achieves an average accuracy (AP) of 89.68% on the SSUTD dataset and 90.19% on the DNASI dataset. These results indicate that our model has excellent detection performance. Full article

Bengala, a steamer that sank in 1889 near Capo Rizzuto, Italy, was a relatively new vessel for its time, with an unusually short 18-year service life, given that steamers of the period typically operated for 30 to 40 years. Despite its brief history, SS Bengala played a significant role in the development of Italy’s young merchant navy, undergoing multiple ownership changes and serving various Italian shipping companies. Employed mainly along the route to Southeast Asia, it transported Italian migrants overseas and also participated in troop raids during the Italian military expedition to Eritrea in 1887. Despite its historical significance, no iconographic material has yet been found to depict SS Bengala, and archival research conducted in Italy and England has not uncovered any naval plans, photographs, or drawings of the ship. To overcome this gap, the authors employed new technologies and historical information to create a virtual reconstruction. This research combined archival sources with underwater surveys, including a detailed 3D survey by divers and archaeologists. Archival research, including consultation of official documents, provided critical information on the ship’s dimensions, superstructure, rigging, materials, and construction methods. The 3D modelling of the ship’s external hull, based on precise geometric data from the wreck site, offers a first step towards virtual reconstruction. The modelling is grounded in photogrammetric surveying techniques, ensuring high accuracy in the reconstruction process. The model can be used in augmented reality (AR) applications to enhance underwater exploration, allowing divers to visualise the reconstructed ship in its original environment. Additionally, it supports museum exhibits, interactive visualisations, and educational games, making it a valuable resource for engaging the public with maritime history and archaeology. Full article

This study aims to address the challenges associated with rockfall assessment and monitoring, focusing on the coastal cliffs of “Navagio Shipwreck Beach” in Zakynthos. A complete time-series analysis was conducted using state-of-the-art methodologies including a 2020 survey using unmanned aerial systems (UASs) and two subsequent surveys, incorporating terrestrial laser scanning (TLS) and UAS survey techniques in 2023. Achieving high precision and accuracy in georeferencing involving direct georeferencing, the utilization of pseudo ground control points (pGCPs), and integrating post-processing kinematics (PPK) with global navigation satellite system (GNSS) permanent stations’ RINEX data is necessary for co-registering the multitemporal models effectively. For the change detection analysis, UAS surveys were utilized, employing the multiscale model-to-model cloud comparison (M3C2) algorithm, while TLS data were used in a validation methodology due to their very high-resolution model. The synergy of these advanced technologies and methodologies offers a comprehensive understanding of rockfall dynamics, aiding in effective assessment and monitoring strategies for coastal cliffs prone to rockfall risk. Full article

Wooden ships on the shipwreck sites are usually only partially preserved, and reconstructing the original hull lines requires considerable effort. The shape of the hull has a direct effect on the ship’s capacity to carry cargo, as well as on its speed and stability. When reconstructing the hull lines, the incomplete nature of the archaeological remains results in the interpretation of the available data. The outcome, therefore, depends on the assumptions and decisions associated with the reconstruction process. This paper examines how the variation in a single parameter, namely, the beam, affects the performance of the vessel. Considering the availability of the model, the Kyrenia ship from the fourth/third century BC is used as a case study. The scope of this paper is to demonstrate and quantify the effect of beam variation on ancient ship performance, namely, the ship cargo capacity, stability, and resistance. Kyrenia ship was used as a study case based on hull lines proposed by Steffy in 1985. The aim is not to modify Steffy’s original reconstruction but to demonstrate that small deviations could significantly affect the performance of the vessel. In addition, an increase in the height of the ship’s sides is proposed as a possible solution to increase the load capacity of the ship. The opportunity to explore a whole set of trials and reconstructive variations with naval engineering software can deepen our understanding of ship performance, allowing us to improve our approach to reconstruction, too. Full article

Organosilicon materials have shown potential as dehydration agents for waterlogged wooden artifacts. These materials can polymerize under normal conditions to form polymers with favorable mechanical strength, antibacterial properties, and aging resistance. However, the insolubility of most organosilicon hindered their penetration into waterlogged wood, which may lead to an unwanted cracking. This study aimed to evaluate the effectiveness of polydimethylsiloxane (PDMS) and hydroxy-terminated polydimethylsiloxane (PDMS-OH) with low viscosity and moderate reactivity for dehydrating waterlogged wooden artifacts from the Nanhai No.1 shipwreck. Four surfactants ((3–aminopropyl) triethoxysilane (APTES), alkyl polyoxyethylene ether (APEO), tri-methylstearylammonium chloride (STAC), and fatty alcohol polyoxyethylene ether (AEO)) and cosurfactant were employed to transform the two kinds of water-repellent silicone oils into eight groups of highly permeable oil-in-water (O/W) emulsions. Under the catalysis of a neutral catalyst, in situ polymerization occurred within the wood cells. Group P2-2 formulated with PDMS-OH and APEO showed the best efficiency in maintaining the dimensions of the wood during dehydration. The dehydrated wood exhibited a natural color and texture with a minimal volume shrinkage rate of 1.89%. The resulting polymer adhered uniformly to the cell walls, effectively reinforcing the wood cell structure. The weight percent gain of the wood was only 218%, and the pores of the cell lumen were well maintained for future retreatment. This method effectively controlled the sol–gel reaction process of the organosilicon and prevented damage to the wooden artifact during the dehydration process. Moreover, the dehydrated wood samples only experienced a low weight gain of 17% at 95% relative humidity (RH), indicating their great environmental stability. Full article

Side-scan sonar (SSS) images present unique challenges to computer vision due to their lower resolution, smaller targets, and fewer features. Although the mainstream backbone networks have shown promising results on traditional vision tasks, they utilize traditional convolution to reduce the dimensionality of feature maps, which may cause information loss for small targets and decrease performance in SSS images. To address this problem, based on the yolov8 network, we proposed a new underwater target detection model based on upsampling and downsampling. Firstly, we introduced a new general downsampling module called shallow robust feature downsampling (SRFD) and a receptive field convolution (RFCAConv) in the backbone network. Thereby multiple feature maps extracted by different downsampling techniques can be fused to create a more robust feature map with a complementary set of features. Additionally, an ultra-lightweight and efficient dynamic upsampling module (Dysample) is introduced to improve the accuracy of the feature pyramid network (FPN) in fusing different levels of features. On the underwater shipwreck dataset, our improved model’s mAP50 increased by 4.4% compared to the baseline model. Full article

Monitoring oil drift by integrating multi-source satellite imagery has been a relatively underexplored practice due to the limited time-sampling of datasets. However, this limitation has been mitigated by the emergence of new satellite constellations equipped with both Synthetic Aperture Radar (SAR) and optical sensors. In this manuscript, we take advantage of multi-temporal and multi-source satellite imagery, incorporating SAR (Sentinel-1 and ICEYE-X) and optical data (Sentinel-2/3 and Landsat-8/9), to provide insights into the spatio-temporal variations of oil spills. We also analyze the impact of met–ocean conditions on oil drift, focusing on two specific scenarios: marine floating oil slicks off the coast of Qatar and oil spills resulting from a shipwreck off the coast of Mauritius. By overlaying oils detected from various sources, we observe their short-term and long-term evolution. Our analysis highlights the finding that changes in oil structure and size are influenced by strong surface winds, while surface currents predominantly affect the spread of oil spills. Moreover, to detect oil slicks across different datasets, we propose an innovative unsupervised algorithm that combines a Bayesian approach used to detect oil and look-alike objects with an oil contours approach distinguishing oil from look-alikes. This algorithm can be applied to both SAR and optical data, and the results demonstrate its ability to accurately identify oil slicks, even in the presence of oil look-alikes and under varying met–ocean conditions. Full article