Matteo Bozzano

Post-earthquake damage assessment of churches is a critical task, due to the architectural complexity and the safety risks associated with on-site inspections, which expose surveyors to potentially dangerous structurally compromised areas. Among visible indicators, cracks represent one of the most relevant features for evaluating structural damage. As part of the wider project RAISE (NRRP-ECS00000035), this study proposes an image-based deep learning approach for automatic crack detection in church buildings, with the aim of supporting traditional survey procedures and reducing both inspection time and operator risk. A convolutional neural network based on transfer learning is developed and evaluated using the Xception architecture. The model is first trained on a large benchmark dataset already available of concrete crack images. Image patches are extracted and classified in a binary framework (crack vs. non-crack), and model performance is assessed using standard metrics such as Precision, Recall, and F1-score. While initial training on concrete images yields satisfactory results (98% accuracy), performance drops significantly when applied to church images, highlighting a strong domain shift. Retraining with church-specific data, a custom dataset collected from churches in the Liguria region, including surfaces characterized by frescoes, plaster, and decorative elements, substantially improves performance. It achieves a Precision of 93.87%, a Recall of 88.45%, and an F1-score of 91.08% on frescoed surfaces of the vaults, a fundamental element in the damage survey phase. Additional tests investigate the influence of lighting conditions, image resolution, and capture distance, providing practical guidance for acquisition protocol design. The results demonstrate the potential of deep learning techniques for supporting crack detection in heritage contexts, while also highlighting current limitations related to data variability and patch-level classification. Future developments will focus on dataset expansion, pixel-level segmentation approaches, and integration with standardized damage assessment frameworks.

The increasing occurrence of flood events in Liguria has highlighted not only the importance of effective techniques for monitoring and preventing such events, but also the need to raise public awareness of these hazards. Moving towards a sustainable future, the importance of educating the younger generation on this topic is becoming increasingly evident as a key agent in the decision-making process. As part of the Outreach group of the Department of Civil, Chemical, and Environmental Engineering at the University of Genoa, we designed hands-on activities for high school students focused on the principles of remote sensing, dedicated to monitoring environmental conditions over time and space, with a focus on vegetation for environmental hazard monitoring as a theme of the Outreach group. The outreach activity is organized in two parts. The first introduces basic concepts of geodesy and Earth observation, including geographic reference systems, positioning, and the role of satellites in determining location and observing the Earth’s surface. The second part involves a hands-on experiment in which students photographed the arrangement of natural objects they had created and computed a simple vegetation index (Green Leaf Index, GLI) to provide an indirect measure of vegetation health condition as a proxy for flood susceptibility. The initiative aims to stimulate curiosity, foster discussion on environmental monitoring, and present remote sensing as an accessible tool for understanding environmental change. The activity has so far been implemented in six scientific high schools in Genoa. An informal post-activity questionnaire and observations by the instructors indicate a high level of student engagement and interest in similar initiatives. While the evaluation is exploratory, the results suggest that simple, low-cost experiments can effectively support outreach in geodesy and environmental monitoring.

River inlets, deltas, and estuaries represent delicate ecosystems highly susceptible to climate change impacts. While significant progress has been made in understanding the morphodynamics of these environments in recent decades, the development of models still requires thorough testing and data integration. In this context, remote sensing emerges as a potent tool, providing crucial data and the ability to monitor temporal changes. In this paper, an integrated approach combining remote sensing and morphodynamic modeling is proposed to assess river systems comprehensively. By utilizing multispectral or RGB optical imagery from satellites or UAVs for river classification and remotely derived bathymetry, echo sounder data for ground truth, and photogrammetric modeling of emerged areas, we outline a procedure to create an integrated and continuous digital terrain model (DTM) of a riverbed, paying particular attention to the wet–dry interface. This method enables us to identify the river centerline, its width, and its slope variations. Additionally, by applying a linear morphodynamic model that considers the spatial variability of river morphology commonly found in estuarine environments, it is possible to predict the wavelength and migration rate of sediment bars. This approach has been successfully applied to recreate the DTM and monitor the morphodynamics of the seaward reach of the Roya River (Italy).

The availability of digital copies of historical artifacts modeling the territory through the so-called “plan-reliefs” is important for many reasons: the preservation of the artifact if the physical object is damaged or destroyed, the possibility of creating virtual showrooms and providing researchers a tool to study the object combining information from different sources. For these reasons, a set of plan-reliefs created during World War I on the Italian front and kept by the Italian Historical War Museum of Rovereto (Italy) was surveyed to create digital models of the surfaces, which were georeferenced in the ETRS89 datum. A set of historical military maps of the same period was georeferenced to overlay the sets to the surface in the digital representation and to try to infer clues about the cartographic sources used in the historical artifact creation. The best transformation for georeferencing the maps is different depending on the map scale, map origin, conservation status and number of Ground Control Points. The georeferencing process precision and accuracy were evaluated. The digital models created in this study were compared to the official Digital Terrain Model (DTM) provided by the Regions or the autonomous provinces. The results demonstrate the feasibility of the approach, and the combination of the models with the georeferenced maps is used by historians to describe the process used in the creation of plan-reliefs.