The Global Landslide Catalog (GLC) was developed with the goal of identifying rainfall-triggered landslide events around the world, regardless of size, impacts or location. The GLC considers all types of mass movements triggered by rainfall, which have been reported in the media, disaster databases, scientific reports, or other sources. The GLC has been compiled since 2007 at NASA Goddard Space Flight Center. This is a unique data set with the ID tag “GLC” in the landslide editor. This dataset on data.nasa.gov was a one-time export from the Global Landslide Catalog maintained separately. It is current as of March 7, 2016. The original catalog is available here: http://www.arcgis.com/home/webmap/viewer.html?url=https%3A%2F%2Fmaps.nccs.nasa.gov%2Fserver%2Frest%2Fservices%2Fglobal_landslide_catalog%2Fglc_viewer_service%2FFeatureServer&source=sd To export GLC data, you must agree to the “Terms and Conditions”. We request that anyone using the GLC cite the two sources of this database: Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52(3), 561–575. doi:10.1007/s11069-009-9401-4. [1] Kirschbaum, D.B., T. Stanley, Y. Zhou (In press, 2015). Spatial and Temporal Analysis of a Global Landslide Catalog. Geomorphology. doi:10.1016/j.geomorph.2015.03.016. [2]

The Global Landslide Catalog (GLC) was developed with the goal of identifying rainfall-triggered landslide events around the world, regardless of size, impacts or location. The GLC considers all types of mass movements triggered by rainfall, which have been reported in the media, disaster databases, scientific reports, or other sources. The GLC has been compiled since 2007 at NASA Goddard Space Flight Center. This is a unique data set with the ID tag “GLC” in the landslide editor. This dataset on data.nasa.gov was a one-time export from the Global Landslide Catalog maintained separately. It is current as of March 7, 2016. The original catalog is available here: http://www.arcgis.com/home/webmap/viewer.html?url=https%3A%2F%2Fmaps.nccs.nasa.gov%2Fserver%2Frest%2Fservices%2Fglobal_landslide_catalog%2Fglc_viewer_service%2FFeatureServer&source=sd To export GLC data, you must agree to the “Terms and Conditions”. We request that anyone using the GLC cite the two sources of this database: Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52(3), 561–575. doi:10.1007/s11069-009-9401-4. [1] Kirschbaum, D.B., T. Stanley, Y. Zhou (In press, 2015). Spatial and Temporal Analysis of a Global Landslide Catalog. Geomorphology. doi:10.1016/j.geomorph.2015.03.016. [2]

The Global Landslide Hazard Distribution is a 2.5 minute grid of global landslide and snow avalanche hazards based upon work of the Norwegian Geotechnical Institute (NGI). The hazards mapping of NGI incorporates a range of data including slope, soil, soil moisture conditions, precipitation, seismicity, and temperature. Shuttle Radar Topography Mission (SRTM) elevation data at 30 seconds resolution are also incorporated. Hazards values less than or equal to 4 are considered negligible and only values 5 through 9 are utilized in further analyses. To ensure compatibility with other data sets, value 1 is added to each of the values to provide a hazard ranking ranging 6 through 10 in increasing hazard. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Norwegian Geotechnical Institute (NGI), and Columbia University Center for International Earth Science and Information Network (CIESIN).

The Global Landslide hazard map is a gridded dataset of landslide hazard produced at the global scale. Landslides happen around the world and have devastating impacts on people and the built environment. To better understand the spatial and temporal distribution of landslide hazard worldwide, the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR) commissioned Arup to undertake a landslide hazard assessment at a global scale. Using a global landslide inventory, landslide susceptibility information provided by NASA, and an innovative machine learning model, our geohazard and risk management experts produced a state-of-the-art quantitative landslide hazard map for the whole world. The dataset comprises gridded maps of estimated annual frequency of significant landslides per square kilometre. Significant landslides are those which are likely to have been reported had they occurred in a populated place; limited information on reported landslide size makes it difficult to tie frequencies to size ranges but broadly speaking would be at least greater than 100 m2. The data provides frequency estimates for each grid cell on land between 60°S and 72°N for landslides triggered by seismicity and rainfall. Applications of this dataset include improved hazard screening based on frequency and severity, consistent national, regional and global scale exposure assessment, estimates of annual expected impact on population and the built environment.

Database of Global Landslide Catalog from NASA collected from 1970 - 2019. It was generated using news feeds and is mostly capturing rain-induced landslide events. More information on metadata and original update source here: https://catalog.data.gov/dataset/global-landslide-catalog-export

This data set is an inventory of some 2800 landslides that occurred in the High Mountain Asia (HMA) study area between 5 January 2007 and 31 December 2018 (plus one event from 28 January 1990). The catalog includes dates and locations of landslides, plus additional characteristics such as event triggers, country, length and area of the slide, and the number of injuries and fatalities.

The events in this catalog represent an HMA-specific subset of the Cooperative Open Online Landslide Repository (COOLR), a project that was created to build a more robust, publicly available inventory of landslides by supplementing data in the NASA Global Landslide Catalog with citizen science reports.

This dataset constitutes one of the outcomes of the Master's Thesis titled 'Landslide identification using deep learning-based change detection and the DeepESDL collaborative cloud platform,' authored by Julia Anna Leonardi, conducted under the supervision of Prof. Maria Antonia Brovelli and Dr. Vasil Yordanov at Politecnico di Milano. The authors extracted the image patches through the DeepESDL platform, which the ESA NoR sponsorship provided access to.

The resource contains the .csv database containing landslide information of 174 events, such as the date, location, source, and the dates of the Sentinel-2 images from before and after the event. The sources of the data included in this inventory are:

• Inventario dei Fenomeni Franosi in Italia (IFFI) compiled by ISPRA
• NASA Global Landslide Catalog (GLC) [1],[2].
• Copernicus Emergency Management Service (© European Union, 2012-2024) [3]
• Geological Survey Ireland | Geohazards | National Landslide Susceptibility Map
• Monsoon triggered landslides in Nepal timed with Sentinel-1 for 2015, 2017, 2018, and 2019, courtesy of Katy Burrows [4]
• Landslides after 2023 earthquakes in Turkey compiled by the NASA GSFC team
• University of Twente online landslide course
• Manually extracted sources from [5], [6], [7], [8].

The .zip file contains the TRAIN dataset with the pre-event Sentinel-2 image patches in folder PRE and the post-event Sentinel-2 image patches in folder POST, and the TEST set with 17 bi-temporal pairs in the PRE and POST folders following the above convention and the labels in the form of change maps in the CM folder. In this updated version the full 13-band Sentinel-2 images are published. (In the previous version only 5 bands (B02, B03, B04, B08, and CLM) were available). All the image patches and the ground truth annotations are in the GeoTIFF format.

The authors developed the dataset for change detection workflows.

Project supported by the ESA Network of Resources Initiative.

This work is funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001

[1] Kirschbaum, D.B., Stanley, T., & Zhou, Y. (2015). Spatial and temporal analysis of a global landslide catalog. Geomorphology, 249, 4-15. doi:10.1016/j.geomorph.2015.03.016

[2] Kirschbaum, D.B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52, 561-575. doi:10.1007/s11069-009-9401-4

[3] Copernicus Emergency Management Service. (n.d.). Retrieved March 12, 2024 from https://emergency.copernicus.eu/

[4] K. Burrows, O. Marcand C. Andermann, “Monsoon triggered landslides in Nepal timed with Sentinel-1 for 2015, 2017, 2018 and 2019”. Zenodo, May 25, 2023. doi: 10.5281/zenodo.7970874.

[5] F. Wang et al., ‘Coseismic landslides triggered by the 2018 Hokkaido, Japan (Mw 6.6), earthquake: spatial distribution, controlling factors, and possible failure mechanism’, Landslides, vol. 16, no. 8, pp. 1551–1566, Aug. 2019, doi: 10.1007/s10346-019-01187-7.

[6] P. Amatya, D. Kirschbaum, and T. Stanley, ‘Rainfall-induced landslide inventories for Lower Mekong based on Planet imagery and a semi-automatic mapping method’, Geoscience Data Journal, vol. 9, no. 2, pp. 315–327, 2022, doi: 10.1002/gdj3.145.

[7] ]‘Vietnam – At Least 12 Killed in Flash Floods and Landslides in North – FloodList’. Accessed: Feb. 18, 2024. [Online]. Available: https://floodlist.com/asia/vietnam-floods-landslides-yen-baison-la-august-2017

[8] L. Colombo, ‘Crollata, causa frana, la volta di una galleria lungo la SP72 a Fiumelatte a Varenna - GLI AGGIORNAMENTI’, Lecco Notizie. Accessed: Feb. 18, 2024. [Online]. Available: https://lecconotizie.com/cronaca/crollata-la-volta-di-una-galleria-lungo-la-sp72-a-fiumelatte-avarenna/

The Global Landslide Mortality Risks and Distribution is a 2.5 minute grid of global landslide mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to landslide hazard. Mortality loss estimates per hazard event are caculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of landslide hazard are obtained from the Global Landslide Hazard Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a relative estimate of landslide-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

🇺🇸 미국

This data set is an open global landslide inventory with input from citizen scientists. The data include the time and location of various landslide events, as well as event characteristics, such as triggers, the number of fatalities, country of occurrence, and the length and area of the slide.

From NASA's Cooperative Open Online Landslide Repository (COOLR). The Cooperative Open Online Landslide Repository (COOLR) is a worldwide database of landslide events. It currently includes NASA’s Global Landslide Catalog (GLC) and landslide events contributed by citizen scientists. In a future release of COOLR, collated landslide inventories will be added by REST API or manually.

Summary of fields included in the Cooperative Open Online Landslide Repository (COOLR).

The Global Landslide Total Economic Loss Risk Deciles is a 2.5 minute grid of global landslide total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP has been spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational Unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by landslide hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

List of all knowna landslide citizen science projects.

The Global Landslide Nowcast addresses the need for real-time situational awareness of landslide hazard. The Landslide Hazard Assessment for Situational Awareness model (LHASA) combines satellite rainfall estimates from the Global Precipitation Measurement mission (GPM) with soil moisture estimates from the Soil Moisture Active Passive (SMAP) satellite and other factors to produce a map of locations where rainfall-triggered landslide activity is probable. Due to the latency of the rainfall data, the nowcast is a near-real time product with a minimum latency of 5 hours. Although the model could be run every half hour, this archive contains a daily record derived from a retrospective model run.The Global Landslide Nowcast version 2.0.0 retains replaces the heuristic decision tree from version 1.0 with a machine learning model. Instead of merging all factors other than precipitation into a susceptibility map, LHASA 2.0 takes in each variable as a separate input layer. The most important change is the replacement of the categorical nowcast with a probabilistic output. This will enable users to adjust the threshold to suit their specific application and geographic location.

The Global Landslide Proportional Economic Loss Risk Deciles is a 2.5 minute grid of landslide hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical Unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

Currently, there are many datasets describing landslides caused by individual earthquakes, and global inventories of earthquake-induced landslides (EQIL). However, until recently, there were no datasets that provide a comprehensive description of the impacts of earthquake-induced landslide events. In this data release, we present an up-to-date, comprehensive global database containing all literature-documented earthquake-induced landslide events for the 249-year period from 1772 through August 2021. The database represents an update of the catalog developed by Seal et al. (2020), which summarized events through March 2020 and was based on the catalog developed by Nowicki Jessee et al. (2020). The revised catalog contains 281 historical earthquakes, 162 of which include documented landslide fatality counts. This represents an addition of 17 earthquakes since the previous version, 9 with documented landslide fatalities, and a removal of 2 duplicate entries. The database includes (where available) information on earthquake size (moment magnitude (Mw), surface-wave magnitude (Ms), and body-wave magnitude (mb)), depth, earthquake fault type, date and time, _location, the availability of a ShakeMap, which estimates the spatial distribution of ground shaking from the USGS ShakeMap system (Worden and Wald, 2016), the availability of a geospatial landslide inventory, information about landslide occurrence (number of landslides, area or volume of landsliding, area affected by landsliding, landslide magnitude), earthquake/landslide impact (total fatalities, landslide fatalities, and number of injuries due to the effects of the earthquake), and USGS Ground Failure Tool estimates (estimated area and population exposed to landsliding). The full dataset of all known landslide-triggering events is provided as “EQIL Database 2022.csv,” including information on the data source(s) for each data component. A subset of the dataset, showing only those events for which landslide fatality counts are available, is provided as “EQIL Database LSFatality 2022.csv.” This subset only includes those columns from "EQIL Database 2022.csv" which are necessary for landslide fatality data analysis and omits columns such as source columns and secondary values.

The Landslide Hazard Assessment for Situational Awareness (LHASA) model identifies locations with high potential for landslide occurrence at a daily temporal resolution. LHASA combines satellite‐based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. When rainfall is considered to be extreme and susceptibility values are moderate to very high, a “nowcast” is issued to indicate the times and places where landslides are more probable. Although the model could be run every half hour, this archive contains a daily record derived from a retrospective model run and spatial coverage is from 60°N to 60°S .

The Landslide Hazard Assessment for Situational Awareness (LHASA) model identifies locations with high potential for landslide occurrence at a daily temporal resolution. LHASA combines satellite‐based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. When rainfall is considered to be extreme and susceptibility values are moderate to very high, a “nowcast” is issued to indicate the times and places where landslides are more probable.This archive contains GeoTIFF Rasters that are a 16-year average (beginning of 2001 - end of 2016). The spatial coverage is from 72°N to 60°S latitude, and 180°W to 180°E longitude, based on IMERG Ver06B from the aforementioned time interval. The provided global maps of exposure to landslide hazards, are at a 30x30 arc-second resolution. These maps show the estimated exposure of population, roads, and critical infrastructure (hospitals/clinics, schools, fuel stations, power stations & distribution facilities) to landslide hazard, as modeled by the NASA LHASA model.The data collection consists of eight files, covering the aforementioned spatial and temporal ranges, totaling approximately 20.3 GB (~2.5 GB each): (1): Landslide hazard (annual average; Units: Nowcasts.yr-1) (2): Landslide hazard (annual standard deviation; Units: Nowcasts.yr-1) (3): Population exposure (annual average; Units: Person-Nowcasts. yr-1. km-2) (4): Population exposure (annual standard deviation; Units: Person-Nowcasts. yr-1. km-2) (5): Road exposure (annual average; Units: Nowcasts.km.yr-1.km-2) (6): Road exposure (annual standard deviation; Units: Nowcasts.km.yr-1.km-2) (7): Critical infrastructure exposure (annual average; Units: Nowcasts.element.yr-1.km-2) (8): Critical infrastructure exposure (annual standard deviation; Units: Nowcasts.element.yr-1.km-2)

Landslide Detection via Satellite Market Outlook

According to our latest research, the global landslide detection via satellite market size reached USD 1.38 billion in 2024, reflecting the market’s rapid adoption of advanced remote sensing technologies for geohazard monitoring. The market is experiencing robust expansion, with a recorded CAGR of 12.7% from 2025 to 2033. By 2033, the market size is forecasted to reach USD 4.08 billion, driven by the increasing frequency of landslide events, heightened government investments in disaster management, and the integration of artificial intelligence with satellite data processing.

A significant growth factor propelling the landslide detection via satellite market is the escalating need for real-time, high-precision monitoring of vulnerable terrains. With climate change intensifying rainfall patterns and accelerating soil erosion, landslide hazards are becoming more frequent and severe, especially in mountainous and hilly regions. Governments, public sector agencies, and private enterprises are prioritizing satellite-based monitoring solutions for their broad coverage, reliability, and ability to provide timely alerts. The deployment of satellite technologies allows for continuous surveillance, even in remote or inaccessible areas, thereby reducing human risk and enabling proactive disaster response. Furthermore, the growing awareness of the economic and social costs associated with landslides has led to increased funding and policy support for advanced detection systems, further accelerating market growth.

Technological advancements in satellite imaging and data analytics are also fueling the expansion of the landslide detection via satellite market. The integration of high-resolution optical imaging, synthetic aperture radar (SAR), and LiDAR technologies has significantly enhanced the accuracy and reliability of landslide detection. These technologies facilitate the identification of subtle ground movements and early-stage slope deformations that precede major landslide events. Moreover, the adoption of artificial intelligence and machine learning algorithms for automated image analysis is streamlining the detection process, reducing false positives, and enabling faster decision-making. As satellite constellations become more affordable and accessible, even developing regions are beginning to utilize these solutions for disaster mitigation and environmental monitoring, broadening the market’s addressable base.

The market is also benefiting from the increasing collaboration between public and private sectors, as well as international organizations, to establish comprehensive landslide early warning systems. Initiatives such as the Global Landslide Catalog and partnerships with space agencies like NASA, ESA, and ISRO are facilitating the sharing of satellite data and best practices across borders. This collaborative approach is leading to the development of standardized protocols for landslide risk assessment and fostering innovation in satellite-based monitoring. Additionally, the growing trend of integrating satellite data with ground-based sensors and IoT networks is further enhancing the robustness of landslide detection systems, enabling multi-layered surveillance and more accurate risk prediction.

Regionally, Asia Pacific dominates the landslide detection via satellite market, accounting for the largest share in 2024, primarily due to the region’s high susceptibility to landslides, particularly in countries like India, China, Nepal, and Indonesia. North America and Europe follow closely, driven by advanced technological infrastructure and strong government initiatives for disaster management and infrastructure protection. Latin America and the Middle East & Africa are emerging markets, gradually increasing their investments in satellite-based geohazard monitoring, supported by international aid and technology transfer programs. Each region presents unique challenges and opportunities, shaping the overall dynamics of the global market.

Component Analysis

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