the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Dec 2025
Unified Global Landslide Catalogue (UGLC): A single, standardised global-scale landslide dataset
Abstract. Landslides are a serious threat to all communities due to their potential for property damage and loss of life. Triggered by different natural, climatic and anthropogenic factors, landslides are complex phenomena and difficult to identify, monitor, and manage (Kirschbaum et al., 2015) https://doi.org/10.1016/j.geomorph.2015.03.016. Accurate and comprehensive data are essential in the mitigation of landslide risk, where both the likelihood and impact of landslides on communities must be quantified. Robust datasets allow for the development of dependable prevention strategies such as land use planning and early warning systems. These proactive measures play a crucial role in landslide risk mitigation(Gomez et al., 2020) https://doi.org/10.1007/s11069-023-05848-8.
This study presents a single global scale standardised landslide catalogue, the Unified Global Landslide Catalogue (UGLC), which is intended as a powerful tool for land risk assessment and management. UGLC integrates multiple open data landslide datasets and reports spatiotemporal data with trigger factors for landslides. Landslide occurrence data are collected from extensive field surveys, GPS data, GIS techniques, satellite imagery, and historical records sourced from government agencies, universities, and researchers.
UGLC contains more than 1 million landslide events as point and polygonal data, from the period spanning circa 1700 to 2023. The catalogue is standardised across 18 field attributes, and systematically grouped into seven main categories: (1) UGLC Reference – a unique event identifier; (2) Source Reference that enables back-tracing to the original data source; (3 and 4) Spatial Accuracy and Temporal Accuracy – precisely describe the geographic location and temporal resolution of recorded events, respectively; (5) Geological Information, including triggering factors; (6) Reliability, which assigns a trustworthiness value to the data; and (7) Notes and Information containing supplementary details such as source links, authorship, scientific publications, and other relevant metadata.
UGLC is intended as a robust catalogue of standardised landslide information worldwide. The aim is to provide a reliable and user-friendly source for the characterisation of landslide occurrence. Uniquely, it presents a comprehensive range of data for global analysis and thus compensates for the shortcomings of small-scale heterogeneous datasets. UGLC will facilitate a deeper understanding of landslide phenomena in relation to the surrounding landscape, climate, and impact on human populations and the built environment (Kirschbaum et al., 2015) https://doi.org/10.1016/j.geomorph.2015.03.016.
- Preprint
(11208 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (extended)
-
CC1: 'Comment on essd-2025-482', feiran ren, 05 Jan 2026
reply
-
AC1: 'Reply on CC1', Saverio Mancino, 05 Jan 2026
reply
Dear Reader,
Thank you very much for your interest and constructive feedback!
To address your questions, as explained in the manuscript, both the point-based and polygonal datasets are distributed as spatial tiles following the grid scheme illustrated in the Zenodo and GitHub repositories (https://zenodo.org/records/16755044/preview/UGLC_tile_grid_map.jpeg?include_deleted=0).
Specifically, the global tiling system is divided into105 tiles: 15 longitudinal steps and 7 latitudinal steps.
But, empty tiles that fall entirely within regions for which no available open landslide data source were available, are automatically excluded from the repository.
For this reason, regions such as China (where open, integrable landslide datasets were currently limited or unavailable) do not yet have data into the UGLC catalog, and consequently corresponding tiles in the UGLC repository.
In future updates, if some open point or polygonal landslide data for China will become available and suitable for integration in the UGLC, new tiles will be generated for those areas and included in the corresponding catalog, and available on UGLC zenodo repository.
To ensure efficient data logistics, accessibility, and consistency with the heterogeneous and often discontinuous spatial availability of source data (as is the case not only for China, but also for parts of Russia, Central Africa, and South America), downloads are currently organized exclusively by tiles containing available data, rather than by geographic regions or national boundaries. If future UGLC releases will achieve a more homogeneous global coverage for both point and polygon datasets, we will certainly consider implementing alternative download options, such as by country or continent.Regarding your observation about some landslide vectors appearing to coincide with road networks, we would like to clarify that potential geographic coordinate offsets or spatial misalignments are explicitly addressed in the point-based catalog through the ACCURACY attribute.
As described in the manuscript, this attribute provides an estimate of positional uncertainty (where possible) based on ancillary information (if available) from the original source catalogs and interpretable as proxies for geospatial error.
The uncertainty is indexed on a scale from 1 (minimum error) to 10 (maximum error), allowing users to account for and filter data according to their specific accuracy requirements.
We thank you again for your thoughtful comment and hope this clarification addresses all your concerns.Sincerely,
The AuthorsCitation: https://doi.org/10.5194/essd-2025-482-AC1
-
AC1: 'Reply on CC1', Saverio Mancino, 05 Jan 2026
reply
-
RC1: 'Comment on essd-2025-482', Anonymous Referee #1, 14 Feb 2026
reply
The manuscript presents the Unified Global Landslide Catalogue (UGLC), a global dataset obtained through the integration and harmonization of numerous pre-existing landslide inventories, catalogues, and archives, with the aim of providing a standardized basis for global-scale analyses, modelling, and risk-management applications.
The initiative to collect and make globally distributed landslide information accessible under an open license undoubtedly represents a significant and commendable effort, consistent with the mission of journals oriented toward data publication. Moreover, the definition of a common ontological structure and standardized attributes constitutes a methodological contribution that could potentially foster interoperability and data sharing at the international level.
However, despite these valuable elements, the work presents substantial critical issues that limit both the scientific robustness of the dataset and its effective reusability for advanced quantitative applications. In particular, key concerns relate to the epistemological heterogeneity of the source data, the absence of independent quantitative validation, spatial, temporal, and informational inconsistencies, and—most importantly—ambiguity regarding the appropriate domains of scientific use.
The data are not new in an observational sense, but have been reorganized and harmonized by the authors to homogenize their attributes. The novelty is therefore structural and ontological rather than empirical. Indeed, the work compiles landslide data from different global sources, making it potentially useful for reconnaissance of available sources, global descriptive analyses, and data-discovery activities; however, their usefulness for quantitative modelling, susceptibility assessment, or machine learning appears limited due to the strong heterogeneity of the original datasets. For example, the Italica dataset cannot be used for certain landslide susceptibility assessments, just as inventories produced for earthquake-induced landslides cannot be used for modelling different from seismic contexts. Some datasets are discontinuous, such as Italica, and therefore cannot be used, for instance, in machine-learning modelling.
The major issue with the UGLC is that unifying attributes is not sufficient to make data homogeneous; it is essential to trace the purpose for which each dataset was generated, because data collection is functional only to its original objective. Mixing datasets collected for different purposes may lead to careless use and the risk of obtaining results that are not truly supported.
“This study offers a conceptual framework and workflow that can serve as a template for the international standardisation of landslide data management and can even be applied in the development of smaller-scale landslide datasets.” As stated above, having an ontologically correct and uniform dataset is not enough to claim that it can be used indiscriminately. The heterogeneity of the data composing the UGLC is so large that, personally, I would find it difficult to use it as a unified dataset.
The description of materials and data sources is overall adequate, and the original datasets are accessible. However, the criteria for semantic normalization, the definition and assignment of reliability, and the procedures for managing uncertainties are less clear. In the methods section, more methodological details are required, including examples of how attributes from the original datasets/catalogues were transferred into the new global catalogue.
Regarding Italian datasets, other published inventories exist that were not considered in the catalogue. How do the authors manage the geographical overlap of data derived from different catalogues/inventories?
Furthermore, it is essential that the authors better clarify the limits of scientific use. Many datasets were created for specific purposes and are not automatically transferable to other analytical contexts. Attribute unification does not make the data scientifically homogeneous, and mixing datasets with different purposes may produce unsupported results.
Is the dataset only partially accessible? All tiles from 8 to 15, from Italy to Japan, are missing for download. Therefore, the dataset described in the article is not fully downloadable. In addition, some links are inaccessible, making the section with links to newspaper reports, for example, useless. This occurs, for instance, for data derived from the Cooperative Open Online Landslide Repository – NASA and from the Global Fatal Landslide Catalog.
No error estimates or sources of uncertainty are provided. Independent quantitative validation is lacking, which unfortunately represents a critical issue for a data paper.
No common quality standards emerge among the integrated datasets.
Each dataset carries the problem of data completeness. Beyond spatial-temporal accuracy, data should also be complete. Do the original datasets describe this aspect? If not, the authors should include a disclaimer. This does not refer to areas where data do not exist, but to situations where data exist but are incomplete. Unfortunately, the declaration of completeness is a limitation of many inventories and catalogues, and this aspect must be clearly stated.
Yes, geographic data standards are followed. Although the global open-access collection effort is appreciable, scientific quality remains limited by heterogeneity, incompleteness, and lack of validation. Currently, the dataset is technically usable only in partial form (only half of the globe). Metadata are not fully adequate. The structure is clear, but technical database validation is missing. The language is consistent, though layout issues occur in Tables 4, 5, and 6.
In practice, reusability for quantitative analyses is limited by intrinsic heterogeneity. Attribute standardization creates an apparent uniformity that does not correspond to real scientific homogeneity. Therefore, the work represents a significant effort toward global integration and standardization of landslide data and constitutes a potentially useful open information infrastructure for synoptic consultation and large-scale descriptive analyses. However, relevant critical issues limit its scientific maturity: epistemological heterogeneity of the data, current absence of quantitative validation, and incompleteness and uncertainty of the information, resulting in limited reusability for modelling. Consequently, the manuscript appears potentially publishable only after substantial revisions, authors should clarify the epistemological limits of the catalogue and strengthen data-quality assessment. I also invite the authors to scale down claims regarding global modelling use. My recommendation is major revision.
Citation: https://doi.org/10.5194/essd-2025-482-RC1
Data sets
Unified Global Landslide Catalogue (UGLC) Saverio Mancino et al. https://zenodo.org/records/16755044
Model code and software
UNIFIED GLOBAL LANDSLIDE CATALOGUE – Point catalogue Saverio Mancino et al. https://github.com/UnibaGEO/UGLC_point
UNIFIED GLOBAL LANDSLIDE CATALOGUE – Polygonal catalogue Saverio Mancino et al. https://github.com/UnibaGEO/UGLC_polygonal
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 672 | 635 | 54 | 1,361 | 24 | 41 |
- HTML: 672
- PDF: 635
- XML: 54
- Total: 1,361
- BibTeX: 24
- EndNote: 41
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Dear Authors,
As a reader of this paper, I appreciate the authors’ efforts in making the dataset publicly available. However, it appears that the data download link provided in the manuscript may be incomplete, as datasets for certain regions, such as China, do not seem to be accessible. In addition, upon examining the available downloadable data, some landslide vectors appear to be located on road networks, which raises the possibility of a geographic coordinate offset or spatial misalignment.
I would like to ask whether it would be possible, if convenient, to provide a complete downloadable link or the dataset covering the China region.
Sincerely,
Reader