The latest progress in deep learning approaches has garnered significant attention across a variety of research fields. These techniques have revolutionized the way marine parameters are measured, enabling automated and remote data collection. This work centers on employing a deep learning model for the automated evaluation of tide and surge, aiming to deliver accurate results through the analysis of surveillance camera images. A mode of deep learning based on the Inception v3 structure was applied to predict tide and storm surges from surveillance cameras located in two different coastal areas of Italy. This approach is particularly advantageous in situations where traditional tide sensors are inaccessible or distant from the measurement point, especially during extreme events that require accurate surge measurements. The conducted experiments illustrate that the algorithm efficiently measures tide and surge remotely, achieving an accuracy surpassing 90% and maintaining a loss value below 1, evaluated through Categorical Cross-Entropy Loss functions. The findings highlight its potential to bridge the gap in data collection in challenging coastal environments, providing valuable insights for coastal management and hazard assessments. This research contributes to the emerging field of remote sensing and machine learning applications in environmental monitoring, paving the way for enhanced understanding and decision-making in coastal regions.

In recent decades, the scientific community has increasingly focused on extreme events linked to climate change, which are leading to more intense and frequent natural disasters. The Mediterranean can be considered a hotspot where the effects of these changes are expected to be more intense compared to other regions of the planet. Italy is not exempt; in fact, with its extensive shoreline, it is particularly vulnerable, especially to high sea levels and coastal erosions. In this framework, from late October to early November 2023, six storm surges occurred in the Gulf of Trieste (NE Italy). These events, characterized by winds from 190°N to 220°N and the significant wave height, which reached up to 1.81 m nearshore—an uncommon meteorological condition in the northern Adriatic Sea—caused the occurrence of eight coastal sinkholes and substantial damages to man-made structures. Thanks to Unmanned Aerial Vehicles (UAVs) and their derived products (high-resolution orthomosaics, Digital Elevation Models—DEMs, and point clouds), it was possible to study these features over time, enabling long-term coastal dynamics monitoring, which can be crucial for timely and effective response and restoration efforts.

Sea level rise (SLR) and increased urbanisation of coastal areas have exacerbated coastal flood threats, making them even more severe in important cultural sites. In this context, the role of hard coastal defences such as promenades and embankments needs to be carefully assessed. Here, a thorough investigation is conducted in Grado, one of the most significant coastal and historical towns in the Friuli Venezia Giulia region of Italy. Grado is located on a barrier island of the homonymous lagoon, the northernmost of the Adriatic Sea, and is prone to flooding from both the sea and the back lagoon. The mean and maximum sea levels from the historical dataset of Venice (1950–2023) were analysed using the Gumbel-type distribution, allowing for the identification of annual extremes based on their respective return periods (RPs). Grado and Trieste sea level datasets (1991–2023) were used to calibrate the statistics of the extremes and to calculate the local component (subsidence) of relative SLR. The research examined the occurrence of annual exceedance of the minimum threshold water level of 110 cm, indicating Grado’s initial notable marine ingression. The study includes a detailed analysis of flood impacts on the urban fabric, categorised into sectors based on the promenade elevation on the lagoon side, the most vulnerable to flooding. Inundated areas were obtained using a high-resolution digital terrain model through a GIS-based technique, assessing both the magnitude and exposure of the urban environment to flood risk due to storm surges, also considering relative SLR projections for 2050 and 2100. Currently, approximately 42% of Grado’s inhabited area is inundated with a sea level threshold value of 151 cm, which occurs during surge episodes with a 30-year RP. By 2100, with an optimistic forecast (SSP1-2.6) of local SLR of around +53 cm, the same threshold will be met with a surge of ca. 100 cm, which occurs once a year. Thus, extreme levels linked with more catastrophic events with current secular RPs will be achieved with a multi-year frequency, inundating more than 60% of the urbanized area. Grado, like Venice, exemplifies trends that may impact other coastal regions and historically significant towns of national importance. As a result, the generated simulations, as well as detailed analyses of urban sectors where coastal flooding may occur, are critical for medium- to long-term urban planning aimed at adopting proper adaptation measures.

1. Coastal transgressive dune systems are large-scale, mobile, partially vegetated dune complexes that can decouple from the backshore and migrate inland over prior terrain, often cannibalising and...

Advances in machine learning and deep learning approaches have drawn substantial interest across diverse research domains, including environmental studies. These innovative techniques have transformed the approaches to measuring marine parameters by facilitating automated and remote data collection. This study focuses on the implementation of a deep learning model to automatically assess tide and surge, aiming for precise outcomes through the analysis of surveillance camera imagery.Utilizing the Inception v3 structure, the deep learning model was applied to predict tide and storm surge from surveillance cameras strategically positioned in two distinct coastal regions, namely Santa Lucia in southeastern Sicily and Lignano Sabbiadoro in Friuli Venezia Giulia, Italy. The deep learning model is based on classification methods to assign a value of water level to a given frame. This approach is particularly advantageous in scenarios where traditional tide sensors face inaccessibility or are distant from measurement points, especially during extreme events demanding accurate surge measurements. The dataset used for the training and validation of the deep learning model covers the entire tide values that could be observed in the study areas. Predictions of the deep learning model were compared with tide gauge values in order to assess the system accuracy.The conducted experiments demonstrate the efficiency of the model in remotely and effectively measuring tide and surge, achieving an accuracy exceeding 90% while maintaining a loss value below 1 for the deep learning model. These findings underscore its potential to fill the data collection gap in challenging coastal environments, offering valuable insights for coastal management and hazard assessment. This study makes an important contribution to the rapidly growing field of remote sensing and machine learning applications in environmental monitoring, facilitating greater comprehension and decision-making in coastal areas.