Landslide data repositories can be broadly categorised into two main types: large-scale landslide datasets and detailed landslide catalogues. Large-scale datasets are systematic spatial and temporal compilations often assembled at national to global scales using relatively standardised methodologies to promote consistency and reproducibility . However, these are frequently characterised by sparse ancillary information, inconsistent thematic content, and heterogeneous spatial coverage, which limits their utility for detailed localised analysis. Causes of such irregularities range from restrictions on regional data access to limited incentives for compiling transnational datasets into open-access global inventories. Furthermore, datasets that document older landslides and those from remote regions are often few in both number and record contained, resulting in a natural under-representation of certain geographic areas and historical periods . Nevertheless, data availability is better at subnational levels, particularly in regions with high landslide susceptibility, as inventories are often more accessible, and the increased attention to the phenomenon drives systematic data acquisition.

In contrast to datasets, landslide catalogues typically represent subnational to local inventories developed from diverse sources and often heterogeneous data classes, including historical archives, scientific literature, and field survey reports. Catalogues tend to provide richer descriptive metadata, such as event triggers, geomorphological characteristics, and impact data but are usually compiled without conformity to any standardised data collection protocols, resulting in considerably variable data structure and quality.

High-quality landslide data organised according to a structured ontology is key to improving the state-of-the-art . With this aim, UGLC assimilates landslide records under a framework of 18 standardised attributes, which promotes semantic consistency and standardised classifications while inheriting the spatial and temporal characteristics of the original datasets. The global catalogue design facilitates integration into GIS-based workflows while preserving the intrinsic heterogeneity of the original inventories, thereby supporting dataset-specific analyses and filtering strategies. In response to increasing exposure to disaster risk and the destructive potential of landslides, UGLC can therefore serve as locus of disparately catalogued global landslide data to aid urgent revisions in spatial planning and disaster management strategies .

The greater focus on landslide-related catastrophes coincides with an increase in remote sensed data availability on such phenomena. Scientists have thus been able to direct research efforts towards advanced data-driven methods and emergent landslide-related models are increasingly calling for spatially explicit but large datasets for accurate simulations, predictions, and mitigation planning. However, large-scale data integration is impeded by substantial differences in how data are collected, structured, and interpreted. In addition to technical differences such as file formats or attribute naming, there are deeper epistemological divergences related to the purpose, methodology, and context of data acquisition. Bringing consistency to the syntax and semantics of cataloguing methods is essential to creating the continental-scale landslide datasets that are now needed for advancing research.

From a semantic perspective, similar landslide phenomena may be classified under different schemes or terminologies across datasets. For example, the same landslide process may be recorded using different naming conventions (e.g., “rock_fall”, “Rockfall”, or “rock fall”), while incomplete or inconsistent attributes (e.g., “unknown”, “null”, or “other”) require explicit handling. In some cases, differences also occur at the interpretative level, such as the classification of “topple” versus “rock fall”. At the same time, syntactic and structural inconsistencies, such as variations in attribute fields, geometry types (points versus polygons), and temporal representation, further complicate direct comparison and integration of data. These differences prevent direct interchangeability across datasets, since each inventory is shaped by its original scientific or operational objectives. As a result, combining multiple sources without a consistent framework can lead to ambiguity, loss of information, or misinterpretation.

A harmonisation strategy that preserves the original meaning of the data while enabling consistent and interoperable data representation should resolve such cataloguing challenges in large scale landslide inventories. These needs and challenges have prompted the methodology developed in this study. We expect that the resulting UGLC will meet the demand for enhanced analysis, including automatic landslide detection and segmentation methods, which are often based on machine learning algorithms applied to high-resolution satellite multispectral imagery or Synthetic Aperture Radar (SAR) interferometry .

Mapping a landslide event typically entails generating points and/or polygon vectors. Points represent key features such as the landslide’s initiation zone, centroid, or depositional terminus. Point-based datasets are usually employed in regional to global inventories where precise delineation is not feasible. Although they are efficient for spatial referencing and statistical aggregation, point representations provide only limited information on the spatial extent and geomorphic footprint of the event. Meanwhile, polygon-based datasets delineate the entire landslide boundary, encompassing both the source and the depositional zones. This format enables more comprehensive spatial analyses, such as volume estimation, terrain deformation modelling, and spatial susceptibility mapping, particularly when high-resolution remote sensing data or detailed field observations are available.

The prevalence of point datasets in global landslide inventories is often due to their ease of generation from widely available imagery and anecdotal event reports. However, poor-resolution imagery and the lack of precise spatial boundaries can inhibit analysis. Moreover, point datasets from different sources are fundamentally inconsistent in terms of data structure, semantic definitions, collection methodologies, and spatial geometries. These inconsistencies pose a significant barrier to transboundary analyses and undermine efforts to construct globally coherent landslide assessments.

A major challenge for the geomorphology research community is to bridge the gap between wide-ranging point datasets and localised and richly detailed catalogues. To this end, the development of a consolidated and standardised global landslide catalogue is essential. Such an effort must involve robust big-data harmonisation strategies that are grounded in a domain-expert-driven ontological framework. This would ensure semantic consistency, structural integrity, and multi-source integration of heterogeneous datasets into a cohesive, analysable whole. In the following sessions, we describe the development of UGLC, starting with a summary of the input data sources and followed by the methods to integrate them within a prescribed ontology.

Table  provides an overview of the 17 point-based landslide catalogues compiled at global, national, and regional levels. Additional technical details of the dataset, including data access links and bibliographic references, are provided in the appendix A . For each constituent dataset, the table reports the acronym, version (where applicable), and total number of recorded events. These datasets differ significantly in terms of data acquisition methodology, spatial resolution, temporal coverage, and thematic scope.

Overall, these point-based datasets reflect a wide range of acquisition techniques, from manual archival interpretation (e.g., IFFI, NTMI) to advanced remote sensing workflows (e.g., NZK, PCLD, RBR).

From a temporal perspective, the datasets cover very different periods: some catalogues collect historical events going back several centuries, such as SLIDO (even before 0) and NTMI (since 1488), while others focus on more recent periods, such as NZK (2016 only) or 1N (2015–2027). Although some offer broad national coverage, e.g. BGS, UAP, others are thematically or regionally focused, capturing specific processes or localised hazard contexts (e.g., CAFLAG, 1N, NZK).

Table  summarises several datasets of landslides mapped in polygonal format, providing essential information on their spatial and temporal extent. In total, it contains 11 global to local landslide datasets distinguishable by their geographical scale. The table reports the dataset acronyms, the geographical coverage, and the number of polygons contained.

These polygonal datasets vary widely in temporal resolution and acquisition strategy, as they span from manual photo-interpretation and field validation (e.g., IFFI, GEUS, PH) to advanced remote sensing and AI-driven classification approaches (e.g., PALI, JLD). Some are tailored to specific triggers and geographies (e.g. MAL, HLD, ASM), while others provide generalised coverage suitable for broader hazard assessments (e.g., ETFGI, COOLR). The appendix B has further technical details and access information for each dataset .

According to our research, this is the first published multilevel ontological methodology specifically developed for landslide phenomena. We are not aware of any similarly structured domain-specific global framework established prior to this work . This unique systematisation was essential for the integration of a highly heterogeneous set of local, national, and global landslide datasets, each characterised by distinct attributes, structures, formats, and original purpose. Expert interpretation was used to harmonise the various data sources, resolve inconsistencies, and standardise attributes by ensuring scientific clarity and usability of the catalogue. Section 4.4 “Data Normalisation” provides a detailed explanation of the normalisation ontology.

Spatio-temporal accuracy is fundamental to developing a reliable landslide catalogue, as it directly affects the quality and reproducibility of derived analyses. To ensure consistency across diverse data sources, UGLC embeds detailed normalised information for each landslide event into 18 well-defined attributes. These attributes are systematically organised into seven principal categories that form the backbone of UGLC's ontological structure:

UGLC Reference: uniquely identifies the event record within the new catalogue;

A complete list of attributes, along with detailed descriptions and classification logic, is provided in Table .

As for Spatial Accuracy, the COUNTRY field identifies the sovereign state where the landslide” or “provinces” are encoded in a separate field. The country name is taken directly from the source data when available and is otherwise derived from coordinates. The ACCURACY field quantifies the estimated spatial error in metres, where such information can be retrieved or inferred. In records where it could not be estimated, it was set as -99999.

Temporal information is managed through the START DATE and END DATE attributes, which store the occurrence date or estimated time window of the event.

Additionally, each record receives a RELIABILITY score (0–10) that weights spatial and temporal accuracy to evaluate overall trustworthiness (see Table ).

The RELIABILITY 1 (highest reliability) to Class 10 (lowest reliability), based on the spatial and temporal precision of each record. Classes are assigned according to predefined thresholds of spatial accuracy and temporal detail, with higher classes reflecting more precise and complete information. This attribute is intended as a practical indicator to support data filtering and selection, rather than a quantitative measure of uncertainty. It does not represent a statistically validated estimate of positional or temporal error. The classification scheme is based on commonly used ranges of spatial precision in geospatial data and reflects typical levels of accuracy associated with landslide mapping practices.

Point-based records can span the full RELIABILITY range (1–10), reflecting variability in both spatial and temporal precision, while polygon geometries are inherently spatially accurate and are therefore limited to classes 1 or 2, where RELIABILITY is primarily determined by temporal precision. This parameter can serve as a filter for users modelling with specific data quality requirements. Classes 1 to 4 offer the most reliable data for detailed analysis and modelling.

The attribute “TYPE” follows an ontology based on the extended Varnes classification , incorporating additional ground instability phenomena, already present in the native datasets.

As for PHYSICAL FACTORS, these are classified according to a standardised framework, which distinguishes between preparatory and triggering factors .

The collection of landslide data was particularly challenging due to the extreme variability of the information within. Native landslide dataset availability, quality, structure, and detail level vary depending on the purpose for which they were collected, often linked to the geographical asset and social context. The data implementation workflow was designed to be highly adaptable, ensuring full compatibility with all native dataset formats (CSV, Shapefile, or Geopackage) and attribute structures. All datasets integrated in UGLC are openly accessible and can be directly downloaded from their respective repositories (or requested from the data provider, as in the case of the BGS National Landslide Database; ).

A preliminary analysis of the datasets revealed heterogeneity and ambiguity, such as inconsistencies in nomenclature, units of measurement, and data formats. For example, a single variable may have had different names, structures, or coding across several datasets, which then required a normalisation process to ensure data consistency and interoperability.

To address this issue, dataset-specific JSON lookup tables were constructed after a deep data exploration to convert non-standard values into a uniform format, and transformation scripts developed with Python libraries to facilitate data normalisation, where needed. The standardised parameters were:

Country: country-of-origin information was introduced through the “assign country to points” function, and countries were linked to the respective event coordinates.

Semantic standardisation was performed through a harmonisation workflow aimed at ensuring structural and semantic consistency while preserving the informational content of the original datasets. Due to the significant heterogeneity of source inventories, the process focused on semantic alignment rather than observational homogenisation. This approach aims to minimise interpretative bias and reduce the risk of semantic misclassification across heterogeneous inventories.

The attribute conversion was governed by lookup tables encoded in JSON format, which define reproducible attribute re-mapping rules between native dataset fields and the UGLC schema. These lookup tables act as preservative conversion dictionaries, prioritising semantic equivalence and information conservation, while avoiding inference when metadata were incomplete or ambiguous.

The lookup tables were designed following an extensive manual data exploration phase. For each inventory, native attributes were systematically analysed to identify the full set of unique classification elements, including inconsistencies, alternative terminologies, and typographical variations. This semantic attribute re-mapping showed how the information was organised in each dataset, rather than forcing it into a predefined classification scheme.

Any interpretative approach was applied only when sufficiently supported by metadata.

As an illustrative example, heterogeneous landslide classification schemes were mapped onto the UGLC TYPE attribute through semantic correspondences, while triggering factors were harmonised within the PHYSICAL FACTOR field when explicitly provided by the source datasets.

Each dataset-specific JSON lookup table is located within the corresponding repository folder and is publicly available, ensuring full methodological transparency and reproducibility.

Points catalogue datasets folders: https://github.com/UnibaGEO/UGLC_point/tree/master/csv (last access: December 2025)

Before any merging operation, all individual records from the various source datasets were consolidated by standardising their formats and harmonising attribute definitions by creating a uniform data structure in which each record was assigned a unique ID to maintain traceability.

Thereafter, duplicate records were identified using a strict matching criterion based on identical spatial coordinates and identical temporal attributes. These cases were interpreted as informational duplicates, where the same landslide event is reported across multiple datasets. When duplicates were detected, the most informative record was retained, based on attribute completeness. In cases of identical records, only one entry was preserved. This conservative approach prioritises traceability and reproducibility over aggressive duplicate removal and does not account for near-duplicate events.

Moreover, to ensure scientific consistency in representing the phenomena of landslide and surface instability, all events classified as “avalanche”, “liquefaction”, and “glacial lake outburst floods” were also excluded, as these processes differ kinematically and geologically from other gravitational phenomena such as landslides. Four output files in two different formats were produced as a result of the merge:

Point dataset called UGLC_point in CSV format.

The lack of a uniform framework poses significant challenges to the integration and reuse of heterogeneous datasets, leading to potential inconsistencies in the interpretation and application of data. Therefore, in this study, the multi-source data were normalised within a common standard for landslide studies to ensure compatibility for wide-ranging applications. The harmonisation process involved detailed analysis and expert judgement to classify key attributes. This critical step produced a coherent and comprehensive representation of landslide records worldwide. The following categories were standardised during the process:

Landslide Type classification: based on the Varnes extended version , this distinguishes six main types of movement according to the nature of the material involved (rock, debris, soil) and the mode of movement (falling, toppling, sliding, flowing, lateral expansion, subsidence). However, in order to better represent the variety and complexity of the phenomena observed in the source catalogues, it was necessary to assimilate similar categories (For example, multiple source labels such as “Rock Fall”, “rockfalling”, “Rock Topple” and “RF” were mapped into a unified “rock fall” class) and include other gravitational ground instabilities phenomena (like sinkholes) in this classification.

Additionally, expert interpretation and corrected re-interpretation of certain types of classification resolved the issue of fragmentary information found in some records. For example, specific phenomena such as riverbank collapse, often omitted or incorrectly categorised from traditional classifications, were included to represent these commonplace events in riverine contexts (Table ).

A major challenge in creating such a large standardised catalogue was achieving information semantic and syntax consistency while condensing significantly heterogeneous data. Extensive inconsistencies in nomenclature are often a barrier to data analysis, especially in fields such as geology. The adopted point and polygon data schema ensured a consistent integration of all datasets while preserving their original geometry types. Thereafter, several statistical analyses were conducted to better understand the content of both catalogues (point and polygonal) and to explore key aspects of the constituent data. This assessment was essential for setting out the framework for appropriate and targeted use of the catalogues and highlighting the potential for future scientific development. In particular, the analysis identified a pronounced disparity in the geographical distribution of landslide records across continents (Figs.  and ).

Within the point catalogue, Europe exhibits the highest proportion of globally recorded events (61.55 %) followed by North America (19.63 %) and Asia (10.17 %). Africa, South America and Oceania collectively constitute a very low share (below 3.97 %). Meanwhile, the polygonal catalogue presents a different distribution pattern, with Asia leading (45.09 %) and followed by Europe (43.40 %) and North America (8.73 %). Here too, Africa, South America, and Oceania collectively constitute a miniscule proportion of recorded events (1.43 %). This imbalance becomes more apparent by going into more detail with a state-by-state analysis, as the landslide records appear in an unbalanced distribution in density and across geography (Fig. ).

In the state-wise density data, it is particularly evident that some relatively small countries like Italy, UK, and New Zealand lead landslide data collection alongside large countries such as the USA and China (and sometimes markedly surpass them, as is the case of Italy, which alone contributes to more than 57 % of the entire catalogue's data). This data distribution evidences greater attention to landslide phenomena in frequently afflicted countries .

Despite the high density of studies in certain landslide-prone regions, data accessibility remains a significant limitation, particularly due to restricted or non-public datasets in various areas, including parts of Europe and Asia. This limited access restricted the scope of the analysis, regardless of the spatial distribution or availability of published research.

Significant variations in time coverage between datasets are also evident. Addressing these discrepancies in formatting and granularity was a significant effort (Fig. ). Addressing temporal inaccuracies involved finding exact event dates and correcting broader temporal ranges (e.g., decades or centuries). For records with incomplete or poorly formatted temporal data, representative time ranges were assigned based on the available context to ensure logical alignment with the recorded phenomena. The approach not only improved temporal consistency, but also enhanced the utility of the catalogue by preserving valuable, albeit imprecise, historical data. Providing temporal consistency in the catalogue also mitigated the risk of data misinterpretation from inconsistent native data formats.

Spatial accuracy is a similarly critical factor in cataloguing landslide phenomena, as it determines the geospatial reliability of each record (Fig. ). Native datasets often present difficulties, including poorly formatted coordinates, varying levels of spatial precision, and inconsistencies in georeferencing methods. The UGLC accuracy distribution ranges from highly precise values (<10 m) to broader approximations (>10 km), and reflects the inherent variability in the quality and reporting practices of the source (Fig. ). As a corollary measure to the standardisation processes, the catalogue's reliability attribute aids in reflecting the general robustness of individual records.

Both the point and polygon catalogues exhibit notably high record reliability, with the majority of entries falling within classes 1 and 2 (see Table ). In the point catalogue specifically, the records with lower reliability classes collectively represent less than 15 % of the total (Fig. ). Nevertheless, the whole spatial and temporal standardisation process forms a reliable framework, which is depicted by the reliability class parameter.

The data analysis of UGLC also presented the spatio-temporal density and landslide type distributions (Fig. ). These data asymmetries are due to the influence of the extremely heterogeneous datasets that were consolidated into standardised landslide types. The integrated product reflects the harmonisation of various terminologies, classification schemes, and granularity, as well as the recovery of valuable data from erroneous event logs.

Landslide types having a significantly higher variance, such as “complex” or “earth flow”, indicate the presence of a higher rate of expert-interpreted data compared to those with natively more unambiguous and consistent types, which are therefore easily interpreted, such as “rockfall” or “sinkhole”. It was also possible to analyse the distribution of various physical factors associated with each recorded landslide (Fig. ). The graph reveals a higher prevalence of missing information about physical factors, followed by Triggering factors (T) and Preparatory factors (P).

The dominance of common trigger factors, such as rainfall and seismic activity, highlights the statistical prevalence of these phenomena in native datasets. These patterns reflect both natural processes and reporting biases associated with data availability and observation practices. However, this distribution of factors is also influenced by the uneven geographical coverage of the data. Areas of frequently documented landslides also tend to be those where certain triggering factors are more prominent. This underscores the need to address spatial heterogeneity in future data collection to enhance global representativeness.

Data completeness is inherited from the original datasets and is generally not explicitly documented. Therefore, the absence of records in a given area should not be interpreted as the absence of landslide occurrence.

Analysis of the overall distribution of the various types of landslide standardised in UGLC shows how the frequencies of each type of landslide vary widely throughout the world (Fig. ). The majority of landslides in the graph fall under undefined categories (“ND”), meaning that native datasets lack information on the kinematics for each landslide record. Nevertheless, for non-null categories, the types “complex”, “earth slide”, “rock fall” and “soil creep” are the most prevalent, while types such as “lahar” and “earth spreading” are minimally represented.

The dataset's spatial variability is apparent through dense clusters found in extensively studied regions, such as South Asia and Central America, which are known for their climatic and geological activity. Conversely, areas such as Africa and Russia are under-represented because of data gaps, likely resulting from mapping challenges or limitations in data access. These analyses underscore the challenge of worldwide data availability and uneven data resolution, which UGLC strives to overcome.

To further explore the spatial distribution of landslide occurrences, UGLC point data were qualitatively compared with global rainfall patterns and seismic hazard. Rainfall intensity was derived from monthly precipitation data based on the GPM IMERG product , aggregated over the period 2000–2025 (Fig. ). Seismic hazard was represented using peak ground acceleration (PGA) from the Global Earthquake Model (GEM; ), corresponding to a 10 % probability of exceed in 50 years under reference site conditions (Vs30 = 760–800 m s⁻¹) (Fig. ).

This comparison is intended as an exploratory assessment rather than a quantitative validation. The observed spatial distribution of landslides shows clear clustering in regions characterised by high rainfall intensity and elevated seismic hazard, consistent with the dominant triggering mechanisms represented in the dataset. At the same time, these patterns reflect not only geomorphological controls but also systematic reporting and data availability biases. Regions subject to frequent rainfall or earthquake-induced landslides are more extensively monitored and documented, leading to higher data density in the catalogue. Conversely, areas with limited monitoring infrastructure or lower reporting capacity remain under-represented.

Given the scale and heterogeneity of the data integrated within the UGLC, it is essential to explicitly acknowledge its epistemological limitations and define appropriate domains of use.

The compilation of landslide datasets from multiple sources presents inherent challenges, primarily due to incomplete or inconsistent metadata, variability in parameter nomenclature, and the absence of universally adopted classification standards for landslide types and associated physical triggers. These issues reflect the diversity of scientific, operational, and institutional contexts in which the original datasets were produced.

The UGLC integrates datasets developed under heterogeneous conditions and for different purposes. As such, it does not constitute a scientifically homogeneous dataset in an observational sense. The harmonisation process standardises the representation of landslide information, but does not eliminate differences in mapping strategies, data acquisition objectives, spatial completeness, temporal coverage, or classification criteria.

Consequently, the UGLC should be interpreted as an organisational and semantic integration layer rather than a uniform analytical dataset. Its primary function is to enable structured access to globally distributed landslide information while preserving the provenance and contextual meaning of the original records.

The catalogue is well suited for data discovery, exploratory analyses, and large-scale descriptive assessments. However, its use in quantitative modelling or predictive applications requires careful, context-aware filtering based on dataset provenance, landslide typology, triggering mechanisms, and data reliability. The dataset should therefore not be treated as a statistically homogeneous input without appropriate preprocessing and domain-specific considerations. It is also important to note that data completeness is inherited from the original datasets and is rarely explicitly documented. As a result, the absence of records in a given region should not be interpreted as the absence of landslide occurrence.

As also highlighted by the comparison with global rainfall and seismic hazard datasets (Sect. 5.1), the spatial distribution of UGLC events reflects both triggering mechanisms and reporting biases, and should therefore be interpreted with caution in quantitative analyses because they may affect frequency statistics, temporal interpretations, and predictive models training.

Despite the integration of multiple global and regional inventories, significant spatial imbalances remain. Large areas, like parts of Africa, South America and North Asia areas are under-represented due to a lack of monitoring infrastructure, logistical constraints, and disparities in data availability and reporting practices. Furthermore, variations in national data-sharing policies and institutional transparency continue to restrict access to landslide information at the global scale.

The RELIABILITY attribute represents a practical classification of spatial and temporal precision but is not a validated accuracy metric. A rigorous validation would require comparison with independent reference data, such as multi-temporal satellite imagery, but this is challenging at the global scale due to dataset heterogeneity, broad temporal coverage, and uneven data availability. A comprehensive validation would therefore require a dedicated effort beyond the scope of this study. Consequently, RELIABILITY should be interpreted as a pragmatic filtering indicator to support appropriate data use.

These limitations highlight the broader challenge of developing globally consistent geohazard datasets. Many existing landslide inventories are designed for regional or national applications and are often not interoperable due to differences in metadata standards and data management practices.

UGLC does not aim to resolve these inconsistencies but provides a structured framework that facilitates interoperability while explicitly preserving data heterogeneity. The workflow and data model presented in this study can therefore serve as a reference for future efforts toward the standardisation of landslide data, both at global and regional scales.

A systematic yearly update will be performed to maintain the integrity and comprehensiveness of UGLC.

This process will include:

Existing data verification: all native landslide datasets integrated into UGLC will be reviewed for updates. Any modifications, corrections, or additions made by the original data providers will be incorporated into the latest version.

Future developments will also focus on creating a dedicated WebGIS portal to improve accessibility and usability for visualisation and new data entry. This platform will facilitate:

Optimised Data Submission: a structured and user-friendly interface will allow for easier submission of new landslide records, including both point-based and polygonal data, in accordance with UGLC standards ensuring consistency across all submitted records;

To further improve landslide data update efforts, UGLC will leverage the OpenStreetMap (OSM) framework and its community for assisted photo-interpreted mapping of landslide polygons. The initiative aims to:

Encourage community participation: engage the OSM community in identifying and mapping landslide-affected areas using satellite imagery and other geospatial datasets.

UGLC provides a structured and interoperable framework for integrating heterogeneous landslide information at the global scale. While the dataset supports exploratory analyses and large-scale investigations, its application in predictive modelling requires careful context-specific filtering strategies and domain-specific interpretation. The catalogue structure, together with attributes describing landslide typology and physical triggering factors where available, enables users to derive analytically consistent subsets suited to specific modelling objectives.

Increasingly available high-resolution geo-environmental datasets, including terrain, hydrological, and soil variables can supplement such modelling applications, and UGLC is designed to be used with these. Datasets such as SoilGrids250m , Geomorpho90m , Hydrography90m , among others, provide geomorphological and environmental descriptors commonly associated with landslide occurrence.

UGLC was indeed conceived as a harmonised landslide information framework designed to facilitate large-scale analyses, comparative investigations, and reproducible modelling experiments. While the catalogue does not eliminate the intrinsic heterogeneity of the original inventories, it provides a consistent semantic and structural basis that can support global-scale exploratory and predictive studies when used with appropriate methodological caution.