Stranded beach litter is a ubiquitous issue. Manual monitoring and retrieval can be cost and labour intensive. Therefore, automatic litter monitoring and retrieval is an essential mitigation strategy. In this paper, we present important foundational blocks that can be expanded into an autonomous monitoring-and-retrieval pipeline based on drone surveys and object detection using deep learning. Drone footage collected on the islands of Malta and Gozo in Sicily (Italy) and the Red Sea coast was combined with publicly available litter datasets and used to train an object detection algorithm (YOLOv5) to detect litter objects in footage recorded during drone surveys. Across all classes of litter objects, the 50%–95% mean average precision (mAP50-95) was 0.252, with the performance on single well-represented classes reaching up to 0.674. We also present an approach to geolocate objects detected by the algorithm, assigning latitude and longitude coordinates to each detection. In combination with beach morphology information derived from digital elevation models (DEMs) for path finding and identifying inaccessible areas for an autonomous litter retrieval robot, this research provides important building blocks for an automated monitoring-and-retrieval pipeline.

The northern region of the Maltese archipelago is experiencing lateral spreading landslide processes. This region is characterized by cliffs with a hard coralline limestone outcropping layer sitting on a thick layer of clay. Such a geological configuration causes coastal instability that results in lateral spreading which predispose to rockfalls and topplings all over the cliff slopes. The aim of this research was to develop a methodology for evaluating cliff erosion/retreat using the integration of geomatics and geophysical techniques. Starting from a 3D digital model of the Selmun promontory, generated by unmanned aerial vehicle (UAV) photogrammetry, it was possible to map the fractures and conduct geophysical measurements such as electrical resistivity tomography and ground penetrating radar for the identification and mapping of vertical fractures affecting the hard coralline limestone plateau, and to create a 3D geological model of the study area. In addition to this, high-accuracy orthophotos from UAV that were captured between 1957 and 2021 were georeferenced into a GIS and compared to aerial and satellite images. The movement and evolution of boulders and cracks in rocks were then vectorized to highlight, track and quantify the phenomenon through time. The results were used to derive a qualitative assessment of the coastal variations in the geometric properties of the exposed discontinuity surfaces to evaluate the volumes and the stop points of the observed rockfalls. The outcomes of this research were finally imported in a GIS which offers an easy approach for the collection and processing of coastal monitoring data. In principle, such a system could help local authorities to address social, economic and environmental issues of pressing importance as well as facilitate effective planning in view of a risk mitigation strategy.

Ambient seismic noise has gained extensive applications in seismology and plays a pivotal role in environmental seismic studies. This study focuses on the Río de la Plata Coastal Plain, employing the horizontal-to-vertical spectral ratio (HVSR) method on ambient seismic noise records to analyze subsurface dynamics. The region’s hydrogeology is complex, featuring partially interconnected coastal aquifers. The HVSR analysis reveals two peaks, with P₀ associated with the sediment-basement interface and P₁ linked to a shallower stratigraphic discontinuity. Temporal analysis of P₁ highlights cyclical patterns correlated with estuarine levels, suggesting a relationship between variations in seismic velocities and tidal dynamics. Comparisons with aquifer data support the hypothesis that tidal variations influence subsurface mechanical properties, impacting the HVSR function. The study hints at the potential of ambient seismic noise analysis as a non-invasive and cost-effective method for studying coastal aquifers and understanding groundwater dynamics. Ongoing research aims to further explore these relationships for enhanced groundwater resource management.

The Wied il-Mielaħ Window (Gozo–Malta) is a limestone natural arch on the north-western coast of the island of Gozo in Malta. It is located at the end of the Wied il-Mielaħ valley north of the village of Għarb. This natural arch is less well known than the Azure Window, which collapsed in March 2017 following a heavy storm, but notwithstanding, it is an imposing and important natural monument too. In the past, the Wied il-Mielah valley was responsible for discharging wastewater from the surrounding localities to the Mediterranean directly at the Wied il-Mielah Window. The sewage flag was often clearly visible underneath the archway into the open sea. The natural features of the arch provide an outstanding touristic attraction. To avoid what happened to the Azure Window, a methodology for the evaluation of the collapse hazard, combining passive seismic, ground penetrating radar (GPR), geological/geomorphological surveys and mine engineering methods, is here proposed. In this study, a methodological approach was applied, based on the following: (i) passive seismic method to study the physical–mechanical characteristics of the rock mass that constitutes the window; (ii) GPR method in order to demonstrate the conservation state (i.e., the intensity of fracturing); (iii) geological/geomorphological surveys in order to obtain a crack pattern; and (iv) scaled span empirical analysis in order to evaluate the stability of the arch. The calculation of the safety factor, with a static method, gave a value equal to 3.75 with a probability of collapse of the marine arch within 50 and 100 years.

Different engineering geological and geophysical investigations were performed at the Sopu promontory in the island of Gozo (Malta), involved in an impressive lateral spreading process due to the superimposition of a stiff limestone (ULC) on a ductile clay (BC). The applied techniques include: traditional geological and engineering geological surveys, unmanned aerial vehicles (UAV) survey, electrical resistivity tomography (ERT) survey, ground-penetrating radar (GPR) investigations, single-station seismic ambient noise measurements, and array seismic ambient noise measurements. The integration of the obtained results allowed us to reconstruct a subsoil model of the promontory that includes features related to the local geology of the slope and to the landslide process, as well as to define a conceptual model that describes the main evolution phases of the expansion process. The presence of back-tilted rock blocks with no features of polarization of Rayleigh waves evidenced the different failure mechanism of the rigid UCL plateau at the Sopu promontory with respect to the Selmun promontory, located in the close island of Malta, where the lateral spreading due to the same geological setting tends to produce unstable rock blocks with a toppling mechanism. This result encourages further future observations and analyses of this topic.

Bathymetric information has become essential to help maintain and operate coastal zones. Traditional in situ bathymetry mapping using echo sounders is inefficient in shallow waters and operates at a high logistical cost. On the other hand, lidar mapping provides an efficient means of mapping coastal areas. However, this comes at a high acquisition cost as well. In comparison, satellite-derived bathymetry (SDB) provides a more cost-effective way of mapping coastal regions, albeit at a lower resolution. This work utilises all three of these methods collectively, to obtain accurate bathymetric depth data of two pocket beaches, Golden Bay and Għajn Tuffieħa, located in the northwestern region of Malta. Using the Google Earth Engine platform, together with Sentinel-2 data and collected in situ measurements, an empirical pre-processing workflow for estimating SDB was developed. Four different machine learning algorithms which produced differing depth accuracies by calibrating SDBs with those derived from alternative techniques were tested. Thus, this study provides an insight into the depth accuracy that can be achieved for shallow coastal regions using SDB techniques.