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.

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<p>Landslide susceptibility assessment is a key topic for land-use planning and for the overall safeguard of human activities. In this perspective, a wide range of methods and techniques have been proposed for the evaluation of landslide susceptibility, ranging from statistical methods to the latest deep learning technologies. Besides, Slope-Area plots are also exploited for the evaluation of surficial processes domains and the increasing availability of digital terrain models with higher resolution allows much detailed analyses.</p><p>On these premises, we compared slope-area plots produced with high resolution Lidar data with a landslide dataset produced following the LOOM data structure. The analysis has been carried out using only surficial phenomena like flows and falls. Moreover, such landslides have been decomposed into their principal components such as detachment, transit, and accumulation zones in order to perform an accurate evaluation of the geomorphic signature of such features. Each landslide has been also compared with the corresponding reference hillslope, defined as the set of enveloping Morse regions computed using Surface Network.</p><p>The plot of slope-area values of the training landslides and their reference hillslopes allows thresholding of the slope values at different contributing area bins, resulting in the mapping of those values exceeding the defined thresholds. Preliminary results show how such defined thresholds based on a proper training dataset could be a valid contribution to the overall topic of landslide susceptibility assessment based on geomorphological criteria at least for surficial landslide types like flow- and fall-like movements.</p>