Extreme Wave Boulders at Premantura: the stamp of the storms in the northern Adriatic Sea

Storms and storm surges could increase their impact on rocky coasts in the next years due to global warming. In recent years, the study of boulders moved by storm waves seems to have played an important role. A key indicator that can follow extreme storm events on rocky shores includes coastal boulders detached from the bedrock. These deposits became in focus of a several studies since play a significant role in coastal hazard assessment. First studies regarded deposits of boulder related to tsunami events, but recently many sites with large clasts related to storm waves have been discovered, also in semi-enclosed shallow basin, such as the Adriatic Sea, in the Mediterranean area. In this study we widened the mapping of boulders recently studied at Premantura promontory (southern Istria, Croatia) in order to better assess the role of coastal morphology in boulder sedimentation. In particular, we present and discuss the role of boulder deposits in several islets close to the promontory, also considering that at the island of Sekovac boulders are definitively of artificial origin and later moved by extreme waves. The investigation of the position, size, and mass of each boulder was created through the application of Uncrewed Aerial Vehicle Digital Photogrammetry (UAV-DP) and Structure from Motion (SfM) technique, defining the GCPS points, and providing high-resolution data. Boulders were identified and categorized using UAV-DP products, and their validation was done through the comparison with outputs of traditional geomorphological investigations such as field surveys including direct axis measurement and the use of Google Earth images for boulder mapping

Trmun (north-eastern Italy): Multi-scale remote and ground-based sensing of a Bronze Age and post-Roman fortification

We have used multi-scale remote sensing to investigate a little known archaeological site in northern Istria (north-eastern Italy). Airborne Laser Scanning (ALS) and archaeological field surveys have allowed us to identify the position and extension of a large Protohistoric hillfort. Its highest and best-preserved sector, corresponding to a modest elevation at the eastern margin of the settlement, has been further investigated through thermal imaging, high-resolution ALS, drone Structure from Motion (SfM) photogrammetry and 3D Ground Penetrating Radar (GPR), leading to a detailed identification of unexpected buried features. An excavation campaign conducted in 2022 has confirmed the remote and ground-based sensing results. This excavation has led to the discovery of a Bronze Age fortification, partially reused and modified with the construction of 2 or 3 square towers during the post-Roman period. Our results demonstrate that the combined analysis of multi-scale remote and ground-based sensing is crucial to planning archaeological exploration in the field. Digital methods provide high-resolution topography and detect buried features that assist in monitoring and managing cultural heritage

Prehistoric Stone Disks from Entrances and Cemeteries of North-Eastern Adriatic Hillforts

The paper presents a group of four, approximately 0.5m large, stone disks from entrances or cemeteries of two protohistoric hillforts of north-eastern Adriatic. The disks, having a sparse chronology with the exception of one dated to the Middle Bronze Age, show flat and plain surfaces or covered with sub-circular depressions. One disk shows two larger cup-marks at the centre of both faces. They are interpreted as ritual artefacts based on the association with sacred settlement locations and comparisons with similar coeval stones found mainly close to citadel entrances, burials and thresholds in the Aegean area and Anatolia

An Artificial Intelligence System for Automatic Recognition of Punches in Fourteenth-Century Panel Painting

In Late-Medieval panel paintings from the Tuscan area, mechanical tools called punches were used to impress repeated motifs on gold foils to create decorative patterns. Such patterns can be used as clues to objectively support the attribution of the paintings, as proposed by art historian Erling S. Skaug in his decades-long study on punches. We investigate the feasibility of employing automatic pattern recognition techniques for accelerating the process of classification of punches by experts working in the field. We propose a system composed of (a) a Convolutional Neural Network for categorizing a punch contained in a frame, and (b) an additional component for uncertainty estimation, aimed at recognizing possible Out-of-Distribution (OOD) samples. After collecting a set of 14th century panel paintings from Tuscany, we train a Convolutional Neural Network which achieves very high test-set accuracy. As far as the uncertainty estimation is concerned, we experiment with two techniques, OpenGAN and II-loss, both exhibiting very positive results. The former seems to work better on specific data extracted from images of panel paintings, while the latter showcases a more consistent behavior when considering additional OOD data obtained randomly. These outcomes indicate that an application of our system in support of experts is feasible, although we subsequently show that additional experiments on larger datasets might be required

An Artificial Intelligence System for Automatic Recognition of Punches in Fourteenth-Century Panel Painting

In Late-Medieval panel paintings from the Tuscan area, mechanical tools called punches were used to impress repeated motifs on gold foils to create decorative patterns. Such patterns can be used as clues to objectively support the attribution of the paintings, as proposed by art historian Erling S. Skaug in his decades-long study on punches. We investigate the feasibility of employing automatic pattern recognition techniques for accelerating the process of classification of punches by experts working in the field. We propose a system composed of (a) a Convolutional Neural Network for categorizing a punch contained in a frame, and (b) an additional component for uncertainty estimation, aimed at recognizing possible Out-of-Distribution (OOD) samples. After collecting a set of 3815 punches from four 14th century panel paintings from Tuscany, we train a Convolutional Neural Network which achieves very high test-set accuracy. As far as the uncertainty estimation is concerned, we experiment with two techniques, OpenGAN and II-loss, both exhibiting very positive results. The former seems to work better on specific data extracted from images of panel paintings, while the latter showcases a more consistent behavior when considering additional OOD data obtained randomly. These outcomes indicate that an application of our system in support of experts is feasible, although we subsequently show that additional experiments on larger datasets might be require

Impact of the October 2018 Storm Vaia on Coastal Boulders in the Northern Adriatic Sea

Boulder detachment from the seafloor and subsequent transport and accumulation along rocky coasts is a complex geomorphological process that requires a deep understanding of submarine and onshore environments. This process is especially interesting in semi-enclosed shallow basins characterized by extreme storms, but without a significant tsunami record. Moreover, the response of boulder deposits located close to the coast to severe storms remains, in terms of accurate displacement measurement, limited due to the need to acquire long-term data such as ongoing monitoring datasets and repeated field surveys. We present a multidisciplinary study that includes inland and submarine surveys carried out to monitor and accurately quantify the recent displacement of coastal boulders accumulated on the southernmost coast of the Premantura (Kamenjak) Promontory (Croatia, northern Adriatic Sea). We identified recent boulder movements using unmanned aerial vehicle digital photogrammetry (UAV-DP). Fourteen boulders were moved by the waves generated by a severe storm, named Vaia, which occurred on 29 October 2018. This storm struck Northeast Italy and the Istrian coasts with its full force. We have reproduced the storm-generated waves using unstructured wave model Simulating WAves Nearshore (SWAN), with a significant wave height of 6.2 m in front of the boulder deposit area. These simulated waves are considered to have a return period of 20 to 30 years. In addition to the aerial survey, an underwater photogrammetric survey was carried out in order to create a three-dimensional (3D) model of the seabed and identify the submarine landforms associated with boulder detachment. The survey highlighted that most of the holes can be considered potholes, while only one detachment shape was identified. The latter is not related to storm Vaia, but to a previous storm. Two boulders are lying on the seabed and the underwater surveys highlighted that these boulders may be beached during future storms. Thus, this is an interesting example of active erosion of the rocky coast in a geologically, geomorphologically, and oceanologically predisposed locality