Mario Valiante

The Martian lava plains are characterized by their extensive, low‐relief surfaces, which are the result of numerous basaltic eruptions and feature a variety of volcanic landforms, including vents, cones, pits, and skylights. The combination of these features and impact craters yields a significant understanding of the planet's geological past and subsurface composition. However, the flat terrain presents a challenge to detailed morphometric analyses, while the unavailability of high‐resolution Digital Elevation Models (DEMs) complicates the precise estimation of radar‐based thicknesses and dielectric constants. To address these challenges, we investigated an ∼8,030 km ² area northwest of Ascraeus Mons within the Tharsis Region. Prior research documented Amazonian volcanic features in this region; however, precise thickness and permittivity values remained undetermined owing to insufficient surface features and limited exposures. An enhanced, multi‐scale neighborhood analysis method, adapted from terrestrial and Martian applications, was utilized to define the boundaries of a shallow lava flow sequence and identify three previously unmapped volcanic vents. Subsequently, all available subsurface sounding radar data in the region were analyzed using a simplified method. The results demonstrate that shallow, low‐relief lava flows overlie a series of laterally consistent subsurface units, suggesting that volcanic and sedimentary processes occurred in multiple phases. The combination of our unique morphometric methodology using radar sounding showcases the characterization of morphologically subtle features despite the constraints of available data. This study offers a methodological framework for the investigation of other low‐relief Martian regions, enhancing our understanding of the planet's volcanic and stratigraphic evolution.

JupyterHub is an open‐source system enabling multiple users to access individual computational environments. This facilitates collaborative development and execution of Jupyter notebooks, Python scripts, and other tools among researchers and educators through a unified interface. Through the integration of container technologies, including Docker, JupyterHub achieves seamless scalability for numerous users while maintaining efficient computational resource management. This flexible approach is especially useful in specialized areas like planetary data science, which requires robust and reproducible workflows to manage large volumes of mission data. The Europlanet Geologic MApping of Planetary surfaces (GMAP) project employs a Docker‐based JupyterHub deployment to centralize essential data processing tools, such as the Integrated Software for Imagers and Spectrometers (ISIS) and the NASA Ames Stereo Pipeline (ASP). These open‐source tools facilitate tasks ranging from image calibration and map projection to stereogrammetry and 3D modeling. The deployment of these elements within Docker containers facilitates simplified installation and consistent performance across disparate hardware configurations. The use of pre‐configured image formats within ISIS, ASP, and other GIS and Python libraries allows planetary scientists to efficiently process raw data into analytical products, including Digital Terrain Models. Additionally, JupyterHub's architecture enables secure collaboration via authentication methods (e.g., OAuth, GitHub), with concurrent provision for private and shared data directories. This integrated framework promotes reproducible research by streamlining the sharing of scripts, notebooks, and workflows. The GMAP JupyterHub platform significantly accelerates scientific discovery through the reduction of technical barriers, the promotion of standardization, and the provision of global access to planetary data science resources.

The Lucanian site of Laurelli represents one of the largest, still poorly investigated, pre-Roman archaeological sites of the Cilento, Vallo di Diano and Alburni Geopark (southern Italy). The site lies on a large, low-relief surface in the upper portion of the Serrapotamo River basin, a right tributary of the Bussento River. Geomorphological, stratigraphical, archaeobotanical and archaeometric analyses were carried out to understand the following: (1) the choice to settle at the site of the town of Laurelli; (2) the origin of the raw material used to build the town; (3) the vegetation present during the lifetime of the town; and (4) the causes of its abandonment. The results indicate that the site was chosen because it was almost stable from a geomorphological point of view, not being affected by deep landslides. Further investigations are needed to unravel the possible occurrence of flooding as a causative event for the abandonment of Laurelli. Carpinus was widespread and used as a fuel for iron production, but the areas from which the iron was extracted have not yet been discovered. The multidisciplinary approach adopted is suitable for application in other archaeological areas worldwide.

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.

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.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.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.

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.

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.