Landslides are damaging and deadly, and they occur in every U.S. state. However, our current ability to understand landslide hazards at the national scale is limited, in part because spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. Landslide inventories are typically collected and maintained by different agencies and institutions, usually within specific jurisdictional boundaries, and often with varied objectives and information attributes or even in disparate formats. The purpose of this data release is to provide an openly accessible, centralized map of existing information on landslide occurrence across the entire U.S. The data release includes digital inventories created by both USGS and non-USGS authors. It provides an integrated database of all the landslides with a selection of uniform attributes, but also includes links to the original digital inventory files (whenever available). Given the wide range of landslide information sources in this data compilation, we also provide an attribute to assess the relative confidence in the characterization of the location and extent of each landslide. Further details about each landslide and more recent information (when it exists) can be accessed by clicking the “more information” attribute link to the original source information. This database will be updated intermittently and was most recently updated in March 2019. Please contact gs-haz_landslides_inventory@usgs.gov for more information on how to contribute additional inventories to this community effort.

Landslides are damaging and deadly, and they occur in every U.S. state. However, our current ability to understand landslide hazards at the national scale is limited, in part because spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. Landslide inventories are typically collected and maintained by different agencies and institutions, usually within specific jurisdictional boundaries, and often with varied objectives and information attributes or even in disparate formats. The purpose of this data release is to provide an openly accessible, centralized map of existing information on landslide occurrence across the entire U.S. The data release includes digital inventories created by both USGS and non-USGS authors. It provides an integrated database of all the landslides with a selection of uniform attributes, but also includes links to the original digital inventory files (whenever available). Given the wide range of landslide information sources in this data compilation, we also provide an attribute to assess the relative confidence in the characterization of the location and extent of each landslide. The confidence level reflects the resolution and quality of input data, as well as the method used for identification and mapping of each landslide. We include a classification attribute for polygons to differentiate between individually mapped landslides and features that are evidence of landsliding. Such evidence may include landslide complexes, Quaternary landslide deposits, alluvial fans, unstable slopes, landslide impacts, and other evidence indicative of landslide occurrence. Further details about each landslide and more recent information (when it exists) can be accessed by clicking the “InventoryURL” attribute link to the original source information. Relative to the initial data release (version 1), this update (version 2) includes more inventories, updated confidence rules, and a new classification attribute. This database will be updated intermittently using the version 2 doi, and was most recently updated in March 2022. Please contact gs-haz_landslides_inventory@usgs.gov for more information on how to contribute additional inventories to this community effort.

Landslides are damaging and deadly, and they occur in every U.S. state. However, our current ability to understand landslide hazards at the national scale is limited, in part because spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. Landslide inventories are typically collected and maintained by different agencies and institutions, usually within specific jurisdictional boundaries, and often with varied objectives and information attributes or even in disparate formats. The purpose of this data release is to provide an openly accessible, centralized map of existing information on landslide occurrence across the entire U.S. The data release includes digital inventories created by both USGS and non-USGS authors. It provides an integrated database of all the landslides with a selection of uniform attributes, but also includes links to the original digital inventory files (whenever available). Given the wide range of landslide information sources in this data compilation, we also provide an attribute to assess the relative confidence in the characterization of the location and extent of each landslide. The confidence level reflects the resolution and quality of input data, as well as the method used for identification and mapping of each landslide. We include a classification attribute for polygons to differentiate between individually mapped landslides and features that are evidence of landsliding. Such evidence may include landslide complexes, Quaternary landslide deposits, alluvial fans, unstable slopes, landslide impacts, and other evidence indicative of landslide occurrence. Further details about each landslide and more recent information (when it exists) can be accessed by clicking the “InventoryURL” attribute link to the original source information. Relative to the initial data release (version 1), this update (version 2) includes more inventories, updated confidence rules, and a new classification attribute. This database will be updated intermittently using the version 2 doi, and was most recently updated in March 2022. Please contact gs-haz_landslides_inventory@usgs.gov for more information on how to contribute additional inventories to this community effort.

1. Abstract Landslides are damaging and deadly, and they occur in every U.S. state. However, our current ability to understand landslide hazards at the national scale is limited, in part because spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. Landslide inventories are typically collected and maintained by different agencies and institutions, usually within specific jurisdictional boundaries, and often with varied objectives and information attributes or even in disparate formats. The purpose of this data release is to provide an openly accessible, centralized map of existing information about landslide occurrence across the entire U.S. This data release is an update of previous versions 1 (Jones and others, 2019) and 2 (Belair and others, 2022). Changes relative to version 2 are summarized in us_ls_v3_changes.txt. It provides an integrated database of the landslides from these inventories (refer to US_Landslide_v3_gpkg) with a selection of uniform attributes, including links to the original digital inventory files (whenever available) (“Inv_URL”). The data release includes digital inventories created by both USGS and non-USGS authors. The original inventory is denoted by an abbreviation in the “Inventory” attribute. The full citation for each abbreviation can be found in us_ls_v3_references.csv. The date of the landslide event is included as a minimum and maximum (“Date_Min” and “Date_Max”) to accommodate events that happen within a range of dates. The date value is inherently difficult to interpret or discern due to the nature of landsliding, where some landslides move for long periods of time or move intermittently, and some areas can exhibit multiple landslide events. To preserve the constituent inventories as much as possible, we include all entries even if they are not considered landslides, such as “gullies” or “avalanche chutes.” We include a landslide type attribute when that information is available (“LS_Type”). The landslide classification system used in the original inventories is not always known or stated in the metadata, but many mapping entities use the schema from Cruden and Varnes (1996) or the updated schema from Hungr and others (2014). Given the wide range of landslide information sources in this data compilation, we provide an attribute to assess the relative confidence in the characterization of the location and extent of each landslide (entry) (“Confidence”). The confidence level reflects the resolution and quality of input data, as well as the method used for identification and mapping. This confidence does not reflect a formal accuracy assessment of field attributes. Relative to the previous data releases (version 1 and 2), this update (v3) includes more inventories, updated confidence rules, a new landslide type attribute, a new unique identifier (“USGS_ID”), new machine-readable date fields, and an ancillary database containing all fields from the original inventories (refer to US_Landslide_v3_ancillary). Please contact gs-haz_landslides_inventory@usgs.gov for more information on how to contribute additional inventories to this community effort. When possible, please cite the constituent inventories as well as this data release. This data release includes: (1) a landslide point file and polygon file in multiple forms (US_Landslide_v3_gpkg, US_Landslide_v3_shp, US_Landslide_v3_csv), (2) an ancillary database with original fields (US_Landslide_v3_ancillary), (3) a spreadsheet that summarizes the confidence rules, their justification, and any extra analyses (us_ls_v3_analyses.csv), (4) a summary file of the changes made between version 2 and version 3 (us_ls_v3_changes.txt), (5) a file containing the references of the constituent inventories (us_ls_v3_references.csv), (6) and a readme (README.txt). Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. 2. Data

1. Abstract Landslides are damaging and deadly, and they occur in every U.S. state. However, our current ability to understand landslide hazards at the national scale is limited, in part because spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. Landslide inventories are typically collected and maintained by different agencies and institutions, usually within specific jurisdictional boundaries, and often with varied objectives and information attributes or even in disparate formats. The purpose of this data release is to provide an openly accessible, centralized map of existing information about landslide occurrence across the entire U.S. This data release is an update of previous versions 1 (Jones and others, 2019) and 2 (Belair and others, 2022). Changes relative to version 2 are summarized in us_ls_v3_changes.txt. It provides an integrated database of the landslides from these inventories (refer to US_Landslide_v3_gpkg) with a selection of uniform attributes, including links to the original digital inventory files (whenever available) (“Inv_URL”). The data release includes digital inventories created by both USGS and non-USGS authors. The original inventory is denoted by an abbreviation in the “Inventory” attribute. The full citation for each abbreviation can be found in us_ls_v3_references.csv. The date of the landslide event is included as a minimum and maximum (“Date_Min” and “Date_Max”) to accommodate events that happen within a range of dates. The date value is inherently difficult to interpret or discern due to the nature of landsliding, where some landslides move for long periods of time or move intermittently, and some areas can exhibit multiple landslide events. To preserve the constituent inventories as much as possible, we include all entries even if they are not considered landslides, such as “gullies” or “avalanche chutes.” We include a landslide type attribute when that information is available (“LS_Type”). The landslide classification system used in the original inventories is not always known or stated in the metadata, but many mapping entities use the schema from Cruden and Varnes (1996) or the updated schema from Hungr and others (2014). Given the wide range of landslide information sources in this data compilation, we provide an attribute to assess the relative confidence in the characterization of the location and extent of each landslide (entry) (“Confidence”). The confidence level reflects the resolution and quality of input data, as well as the method used for identification and mapping. This confidence does not reflect a formal accuracy assessment of field attributes. Relative to the previous data releases (version 1 and 2), this update (v3) includes more inventories, updated confidence rules, a new landslide type attribute, a new unique identifier (“USGS_ID”), new machine-readable date fields, and an ancillary database containing all fields from the original inventories (refer to US_Landslide_v3_ancillary). Please contact gs-haz_landslides_inventory@usgs.gov for more information on how to contribute additional inventories to this community effort. When possible, please cite the constituent inventories as well as this data release. This data release includes: (1) a landslide point file and polygon file in multiple forms (US_Landslide_v3_gpkg, US_Landslide_v3_shp, US_Landslide_v3_csv), (2) an ancillary database with original fields (US_Landslide_v3_ancillary), (3) a spreadsheet that summarizes the confidence rules, their justification, and any extra analyses (us_ls_v3_analyses.csv), (4) a summary file of the changes made between version 2 and version 3 (us_ls_v3_changes.txt), (5) a file containing the references of the constituent inventories (us_ls_v3_references.csv), (6) and a readme (README.txt). Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. 2. Data

This inventory was originally created by Harp and others (1984) describing the landslides triggered by a sequence of earthquakes, with the largest being the M 6.5 Mammoth Lakes, California earthquake that occurred on 25 May 1980 at 19:44:50 UTC. Care should be taken when comparing with other inventories because different authors use different mapping techniques. This inventory includes landslides triggered by a sequence of earthquakes rather than a single mainshock. Please check the author methods summary and the original data source for more information on these details and to confirm the viability of this inventory for your specific use. With the exception of the data from USGS sources, the inventory data and associated metadata were not acquired by the U.S. Geological Survey (USGS) and thus have not been reviewed for accuracy and completeness by the USGS. They are presented as part of this data series for convenience of the user only, as part of an effort to make published ground-failure inventories more accessible from a single aggregated site. No warranty, expressed or implied, is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.

This inventory was originally created by Harp and others (1984) describing the landslides triggered by a sequence of earthquakes, with the largest being the M 6.5 Mammoth Lakes, California earthquake that occurred on 25 May 1980 at 19:44:50 UTC. Care should be taken when comparing with other inventories because different authors use different mapping techniques. This inventory includes landslides triggered by a sequence of earthquakes rather than a single mainshock. Please check the author methods summary and the original data source for more information on these details and to confirm the viability of this inventory for your specific use. With the exception of the data from USGS sources, the inventory data and associated metadata were not acquired by the U.S. Geological Survey (USGS) and thus have not been reviewed for accuracy and completeness by the USGS. They are presented as part of this data series for convenience of the user only, as part of an effort to make published ground-failure inventories more accessible from a single aggregated site. No warranty, expressed or implied, is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.

Earthquake-triggered ground-failure, such as landsliding and liquefaction, can contribute significantly to losses, but our current ability to accurately include them in earthquake hazard analyses is limited. The development of robust and transportable models requires access to numerous inventories of ground failure triggered by earthquakes that span a broad range of terrains, shaking characteristics, and climates. We present an openly accessible, centralized earthquake-triggered ground-failure inventory repository in the form of a ScienceBase Community to provide open access to these data, and help accelerate progress. The Community hosts digital inventories created by both USGS and non-USGS authors. We present the original digital inventory files (when available) as well as an integrated database with uniform attributes. We also summarize the mapping methodology and level of completeness as reported by the original authors for each inventory. This document describes the steps taken to collect, process, and compile the inventories and the process for adding additional ground-failure inventories to the ScienceBase Community in the future.

Landslide susceptibility models show the potential of landslide occurrence at a location. These models are pivotal for reducing losses associated with landslides (Godt et al., 2022). In this data release, we include susceptibility results from the associated manuscript by Woodard and Mirus (2025). This manuscript shows how a morphometric model can create consistent and effective susceptibility models over large regions (> 100 km2) by analyzing the terrain’s topography. The model assumes that areas with high relative slope and hillslope area in comparison to the rest of the terrain are more susceptible to landsliding. As the model’s only input is elevation data, it mitigates the data biases common in the data-driven statistical methods (e.g., machine learning) generally used over these scales. We compare the morphometric model outputs to a parsimonious national susceptibility map and logistic regression machine learning models. The national susceptibility map is available in Belair et al., (2024). The two logistic regression models are trained on the landslide data available in the Willamette Valley Hydrologic Unit Code (HUC) 4 watershed (DOGAMI, 2024). To account for the effects of the sampling ratio of event to non-event data points, we create two logistic regression models. The first uses a 1:1 sampling ratio of landslide to non-landslide points and the second uses all the data within the training data which results in a 1:33 sampling ratio. Environmental datasets requisite for the logistic regression models are all derived from the three-dimensional elevation program (3DEP) (U.S. Geological Survey, 2019a) preprocessed within the National Hydrography Dataset (U.S. Geological Survey, 2019b). The morphometric model was derived using only the 3DEP dataset without any input of where landslides have occurred. All model outputs are shown with slope units. This data release includes the following files: 1) logistic regression results with 1:1 sampling ratio over Willamette Valley HUC4 watershed (1709) (Logistic_1709_1.zip); 2) logistic regression results with 1:33 sampling ratio over Willamette Valley HUC4 watershed (1709) (Logistic_1709_All.zip); 3) morphometric results with uniform weights over the Willamette Valley HUC4 watershed (1709) (Morph_Uniform_1709.zip); 4) morphometric results with area weights over the 1701 HUC 4 watershed (Morph_Area_1701.zip); 5) morphometric results with area weights over the 1702 HUC 4 watershed (Morph_Area_1702.zip); 6) morphometric results with area weights over the 1703 HUC 4 watershed (Morph_Area_1703.zip); 7) morphometric results with area weights over the 1704 HUC 4 watershed (Morph_Area_1704.zip); 8) morphometric results with area weights over the 1705 HUC 4 watershed (Morph_Area_1705.zip); 9) morphometric results with area weights over the 1706 HUC 4 watershed (Morph_Area_1706.zip); 10) morphometric results with area weights over the 1707 HUC 4 watershed (Morph_Area_1707.zip); 11) morphometric results with area weights over the 1708 HUC 4 watershed (Morph_Area_1708.zip); 12) morphometric results with area weights over the 1709 HUC 4 watershed (Morph_Area_1709.zip); 13) morphometric results with area weights over the 1710 HUC 4 watershed (Morph_Area_1710.zip); 14) morphometric results with area weights over the 1711 HUC 4 watershed (Morph_Area_1711.zip); 15) morphometric results with area weights over the 1712 HUC 4 watershed (Morph_Area_1712.zip). 16) shape file field descriptors (Field_Descriptors.txt) Each zip-file contains the vector shapefiles of interest which can be extracted using most archiver software. References Cited DOGAMI. (2024). SLIDO (Version 4.5) [Data set]. https://pubs.oregon.gov/dogami/SLIDO/4.5/SLIDO_Release_4p5_wMetadata.gdb.zip. Gina M Belair, Jeanne M Jones, Sabrina N Martinez, Benjamin B Mirus, & Nathan J Wood. (2024). Slope-Relief Threshold Landslide Susceptibility Models for the United States and Puerto Rico [Data Release]. U.S. Geological Survey.

Landslide susceptibility models show the potential of landslide occurrence at a location. These models are pivotal for reducing losses associated with landslides (Godt et al., 2022). In this data release, we include susceptibility results from the associated manuscript by Woodard and Mirus (2025). This manuscript shows how a morphometric model can create consistent and effective susceptibility models over large regions (> 100 km2) by analyzing the terrain’s topography. The model assumes that areas with high relative slope and hillslope area in comparison to the rest of the terrain are more susceptible to landsliding. As the model’s only input is elevation data, it mitigates the data biases common in the data-driven statistical methods (e.g., machine learning) generally used over these scales. We compare the morphometric model outputs to a parsimonious national susceptibility map and logistic regression machine learning models. The national susceptibility map is available in Belair et al., (2024). The two logistic regression models are trained on the landslide data available in the Willamette Valley Hydrologic Unit Code (HUC) 4 watershed (DOGAMI, 2024). To account for the effects of the sampling ratio of event to non-event data points, we create two logistic regression models. The first uses a 1:1 sampling ratio of landslide to non-landslide points and the second uses all the data within the training data which results in a 1:33 sampling ratio. Environmental datasets requisite for the logistic regression models are all derived from the three-dimensional elevation program (3DEP) (U.S. Geological Survey, 2019a) preprocessed within the National Hydrography Dataset (U.S. Geological Survey, 2019b). The morphometric model was derived using only the 3DEP dataset without any input of where landslides have occurred. All model outputs are shown with slope units. This data release includes the following files: 1) logistic regression results with 1:1 sampling ratio over Willamette Valley HUC4 watershed (1709) (Logistic_1709_1.zip); 2) logistic regression results with 1:33 sampling ratio over Willamette Valley HUC4 watershed (1709) (Logistic_1709_All.zip); 3) morphometric results with uniform weights over the Willamette Valley HUC4 watershed (1709) (Morph_Uniform_1709.zip); 4) morphometric results with area weights over the 1701 HUC 4 watershed (Morph_Area_1701.zip); 5) morphometric results with area weights over the 1702 HUC 4 watershed (Morph_Area_1702.zip); 6) morphometric results with area weights over the 1703 HUC 4 watershed (Morph_Area_1703.zip); 7) morphometric results with area weights over the 1704 HUC 4 watershed (Morph_Area_1704.zip); 8) morphometric results with area weights over the 1705 HUC 4 watershed (Morph_Area_1705.zip); 9) morphometric results with area weights over the 1706 HUC 4 watershed (Morph_Area_1706.zip); 10) morphometric results with area weights over the 1707 HUC 4 watershed (Morph_Area_1707.zip); 11) morphometric results with area weights over the 1708 HUC 4 watershed (Morph_Area_1708.zip); 12) morphometric results with area weights over the 1709 HUC 4 watershed (Morph_Area_1709.zip); 13) morphometric results with area weights over the 1710 HUC 4 watershed (Morph_Area_1710.zip); 14) morphometric results with area weights over the 1711 HUC 4 watershed (Morph_Area_1711.zip); 15) morphometric results with area weights over the 1712 HUC 4 watershed (Morph_Area_1712.zip). 16) shape file field descriptors (Field_Descriptors.txt) Each zip-file contains the vector shapefiles of interest which can be extracted using most archiver software. References Cited DOGAMI. (2024). SLIDO (Version 4.5) [Data set]. https://pubs.oregon.gov/dogami/SLIDO/4.5/SLIDO_Release_4p5_wMetadata.gdb.zip. Gina M Belair, Jeanne M Jones, Sabrina N Martinez, Benjamin B Mirus, & Nathan J Wood. (2024). Slope-Relief Threshold Landslide Susceptibility Models for the United States and Puerto Rico [Data Release]. U.S. Geological Survey.