LOOM (landslide object-oriented model) is here presented as a data structure for landslide inventories based on the object-oriented paradigm. It aims at the effective storage, in a single dataset, of the complex spatial and temporal relations between landslides recorded and mapped in an area and at their manipulation. Spatial relations are handled through a hierarchical classification based on topological rules and two levels of aggregation are defined: (i) landslide complexes, grouping spatially connected landslides of the same type, and (ii) landslide systems, merging landslides of any type sharing a spatial connection. For the aggregation procedure, a minimal functional interaction between landslide objects has been defined as a spatial overlap between objects. Temporal characterization of landslides is achieved by assigning to each object an exact date or a time range for its occurrence, integrating both the time frame and the event-based approaches. The sum of spatial integrity and temporal characterization ensures the storage of vertical relations between landslides, so that the superimposition of events can be easily retrieved querying the temporal dataset. The here proposed methodology for landslides inventorying has been tested on selected case studies in the Cilento UNESCO Global Geopark (Italy). We demonstrate that the proposed LOOM model avoids data fragmentation or redundancy and topological inconsistency between the digital data and the real-world features. This application revealed to be powerful for the reconstruction of the gravity-induced deformation history of hillslopes, thus for the prediction of their evolution.

The automated recognition of landforms holds significant importance within the framework of digital geomorphological mapping, serving as a pivotal focal point for research and practical applications alike. Over the last decade, various methods have been developed to achieve this goal, ranging from grid-based to object-based approaches, covering a range from supervised to completely unsupervised techniques. Furthermore, the vast majority of the methods mentioned depend on Digital Elevation Models (DEMs) as their primary input, highlighting the crucial significance of meticulous preparation and rigorous quality assessment of these datasets. In this study, we compare the outcomes of grid-based methods for landforms extraction and surficial process type assessment, leveraging various DEMs as input data. Initially, we employed a photogrammetric Digital Terrain Model (DTM) generated at a regional scale, along with two LiDAR datasets. The first dataset originates from an airborne survey conducted by the national government approximately a decade ago, while the second dataset was acquired by UAV as part of this study’s framework. The results highlight how the higher resolution and level of detail of the LiDAR datasets allow the recognition of a higher number of features at higher scales; but, in contrast, generally, a high level of detail corresponds with a higher risk of noise within the dataset, mostly due to unwanted natural features or anthropogenic disturbance. Utilizing these datasets for generating geomorphological maps harbors significant potential in the framework of natural hazard assessment, particularly concerning phenomena associated with geo-hydrological processes.

The assessment and mitigation of landslide risk affecting hillslopes in highly urbanized and infrastructured environments are often problematic due to the inadequacy of the traditional approach based on landslide inventories and the absence of a shared language between the different scientific-technical operators (geologists, engineers, architects, environmentalists, economists, jurists) and recurrent understanding problems with policymakers, stakeholders, and property owners. Therefore, innovative technologies and working procedures are required to address these problems. In this context, the European INSPIRE Directive and the Italian national Catalog of Territorial Data with the related Geo-Topographic DB provide positive responses in terms of data standardization and transdisciplinary interoperability. On the other hand, the application of the object-oriented geomorphological mapping of landslides and, even more, the recently proposed Landslide Object-Oriented Model (LOOM) make it possible to develop a more thorough approach to assess the spatial and temporal relationships between landslides and affected slopes. Following the above perspective, the InterUniversity Research Center for Prevision and Prevention of Great Risks (C.U.G.RI.) produced the LOOM-based “eventory” of landslides over a sector of the Tyrrhenian coastal belt, northwest of Salerno city, in the framework of a multi-disciplinary investigation project launched by the Campania Regional Administration to assess the landslide risk. The quantitative assessment of the geomorphological expert-judgment procedures has been carried out exploiting morphometric indexes: the Topographic Position Index (TPI) for automatic slope features recognition, and the Slope-Area plots for surficial process domains. Furthermore, the application of the INSPIRE, and related Italian National Geo-Topographic DB standards allowed transdisciplinary interaction between scientists, technicians, and managers. Such proposal can support the risk management procedure, adding in the Value Judgement and Risk Tolerance Criteria simplicity and effective interoperability in trans-disciplinary frameworks.