Landslides triggered by heavy rainfall pose significant threats to human settlements and infrastructure in temperate and equatorial climate regions. This study focuses on the development of the Open Landslide Project (OLP), an open source landslide inventory aimed at facilitating geostatistical analyses and landslide risk management. Using a multidisciplinary approach and open source, multisatellite imagery data, more than 3000 landslides triggered by the extreme rainfall of autumn 2019 in northwestern Italy were systematically mapped. The inventory creation process followed well-defined criteria and underwent rigorous validation to ensure accuracy and reliability. The dataset’s suitability was confirmed through multivariate correlation and Double Pareto probably density function. The OLP inventory effectiveness in assessing landslide risks was proved by the development of a landslide susceptibility model using binary logistic regression. The analysis of rainfall and lithology revealed that regions with lower rainfall levels experienced a higher occurrence of landslides compared to areas with higher peak rainfall. This was attributed to the response of the lithological composition to rainfalls. The findings of this research contribute to the understanding and management of landslide risks in anthropized climate regions. The OLP has proven to be a valuable resource for future geostatistical analysis.

This study introduces and tests the MAGUS (Model for the Analysis of Geomorphological Urban Systems), a GIS-based methodological framework designed to analyze the complex interplay between urbanization and natural landscapes. Focusing on the historical evolution of Turin city center (Italy), the research employs an interdisciplinary approach to examine how urban expansion has interacted with pre-existing geomorphological features. The study leverages historical documents, digitized and georeferenced within a cartographic space, to reveal the relationships between natural and anthropogenic landforms from the Roman period through the 20th century. A key innovation of MAGUS is the integration of Multi-Temporal Markers (MTM) and Volumetric Unit of Building (VUB), which enhances the accuracy of historical data analysis. The results demonstrate that urbanization significantly alters the natural landscape in the historic city center of Turin, where the stratification of urban expansion phases partially obscures the original landform. By overlaying topographical data with historical urban expansion maps, the research highlights the concealed geomorphological elements beneath Turin’s urban fabric. This interdisciplinary study not only contributes to the field of urban geomorphology but also provides a foundation for future research into the impacts of urbanization on natural landscapes, which is increasingly critical as urban populations continue to grow.

Field-based landslide mapping is a crucial task for geo-hydrological risk assessment but is often limited by the lack of integrated tools to capture accurate spatial and temporal data. This research investigates a Direct Numerical Cartography (DNC) system’s ability to capture both spatial and temporal landslide features during fieldwork. DNC enables fully digital surveys, minimizing errors and delivering real-time, spatially accurate data to experts on site. We tested an integrated approach combining the Ground Operative System for GIS Input Remote-data Acquisition (GOGIRA) with the Expert-based Multitemporal AI Detector (ExMAD). GOGIRA is a low-cost system for efficient georeferenced data collection, while ExMAD uses AI and multitemporal Sentinel-2 imagery to detect landslide triggering times. Upgrades to GOGIRA’s hardware and algorithms were carried out to improve its mapping accuracy. Field tests in Western Greece compared data to 64 expert-confirmed landslides, with the Range-R device showing a mean spatial error of 50 m, outperforming the tripod-based UGO device at 82 m. Operational factors like line-of-sight obstructions and terrain complexity affected accuracy. ExMAD applied a pre-trained U-Net convolutional neural network for automated temporal trend detection of landslide events. The combined DNC and AI-assisted remote sensing approach enhances landslide inventory precision and consistency while maintaining expert oversight, offering a scalable solution for landslide monitoring.

On large landslide areas, two-dimensional and three-dimensional geological-technical models realization require a large number of subsurface data.We investigate a complex landslide system located in San Vito Romano, Central Italy, 40 km east from Rome where a large number of boreholes, piezometers and geophysical surveys are available. The purpose of this work is the San Vito Romano landslide characterization in order to create a simplified graphic 3D model and to support a monitoring plan. The aim is also to support local authorities in civil protection activities.The geological context is characterised by a Tortonian sequence of turbidite deposits, characterised by marls and arenaceous intercalations, forming a monocline with 15-20° dip-direction eastward, parallel to slope inclination. Moreover, a complex hydrogeological system characterises the groundwaters.This landslide has a spatial extent of about 0.5 km2 and it has been studied for lot of years. It affects San Vito Romano’s new town (built from the 60s) and it has been interpreted as a large rock translational slide. From a geomorphological point of view the village is located along a cuesta. Human activities consist in buildings, roads and public services, built over the years, even in the recent past.A multitude of technical reports were carried out in this area during the last decades: geological surveys for building projects, geotechnical surveys for landslide monitoring planning, academic studies and field survey to understand the geomorphological slope evolution, hydrogeological and geophysical survey.All the available surveys were censored in order to create a large database in GIS environment. The database containing all the information from 80 linear and punctual surveys.Therefore, a boreholes surveyed quick interpretation was carried out. First, the stratigraphy was simplified into three different lithological units: loose material belonging to the landslide, bedrock involved in the gravitational process and bedrock in place. The stratigraphic and geotechnical data were implemented by the seismic data.A Digital Terranean Model was created using contour lines and elevation points from a 1:2000 scale local topographic map.All available data has been entered into AutoCAD Map 3D and georeferenced in GIS environment. 7 E-W and 8 S-N cross sections were realized allowing a first two-dimensional landslide system interpretation. Finally, the cross sections were correlated to create a single simplified three-dimensional subsurface model.This model shows at least three surfaces of rupture at different depth and the geological setting of the wide translation slide. Moreover, it could be implemented with new data and it could be imported into slope stability and hydrogeological modelling software for numerical analysis.