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227 results for “landslides”
Inventory of landslides triggered by heavy rainfall in the Emilia-Romagna region (Italy) in May 2023
<p>The dataset contains 49103 landslides, that were manually mapped by visual inspection of pre- and post-event satellite images in an area of 8981 km2. Such images are acquired by PlanetScope satellites (<a href="https://www.planet.com/">https://www.planet.com/</a>) and are provided under an academic license; 3-m resolution multiband tiles are used.</p> <p>Pre-event imagery refers to the Monthly Global Basemap products provided by Planet, the April 2023 Basemap was used. Post-event images were acquired between 22 May and beginning of June 2023. The cloud-free image closer to the event was used and multi-temporal frames were checked in selected areas (e.g., due to the presence of shadows or unclear images). Images are accessed through the Planet QGIS Plugin.</p> <p>This dataset supersedes version 1, since it represents its update; major changes include:</p> <ul> <li>mapping over a wider area (8981 vs 5764 km2);</li> <li>check on the landslides mapped in version 1 located on flat slopes (lower than 5°); removal of polygons associated with river erosion and not due to gravity movements</li> </ul> <p> </p> <p>NOTES ON VERSION 1</p> <p>landslides were manually mapped at a scale of 1:5.000 by a single operator in a time interval of 5 weeks following the rainfall event; the inventory (version 1.0) was completed on 28 June 2023. Please note that data did not undergo any kind of validation.</p> <p>Data are provided in shapefile format (coordinate system WGS84 UTM 32N) and in kml format.</p> <p>The main dataset is the “Emilia landslides” shp/kml file; the “area” shapefile refers to the investigated area; the “riverbank and agricultural fields” files include polygons that were mapped but refer either to river courses having high discharge in the post-event images, or to color changes probably due to farming activities or the evolution of agricultural fields. The “riverbank and agricultural fields” elements should not refer to slope movements, and usage of these data is not recommended, unless a validation is made.</p>
Landslide Susceptibility in the North Tanganyika-Kivu Rift Region
<p>The map is derived from the ensemble landslide susceptibility model as presented in the paper entitled "The added value of a regional landslide susceptibility assessment: The western branch of the East African Rift". This work was published in Geomorphology (https://doi.org/10.1016/j.geomorph.2019.106886). Refer to this paper when using this map.</p> <p>The map covers parts of the eastern DRC, Rwanda, and Burundi.</p> <p>The map is provided in .tif format and displays values between 0 (low susceptibility) and 1000 (high susceptibility). The coordinate reference system is EPSG:4326 - WGS84.</p>
Preliminary Canadian Landslide Database
<p><span lang="EN-US">This preliminary Canadian landslide database is a publicly available compilation of existing landslide inventories and original mapping. Version 12.0 of the database contains 25,500 entries of both landslide events (discrete recorded period of movement) and landslide features (slope with morphology consistent with past or ongoing movement). Landslide locations are provided as point features and include attributes for landslide type, material type (surficial, rock, ice, anthropogenic), point location type (headscarp, source, transport, deposit), qualitative location confidence (low, moderate, high), and a field for tracking updates to an entry. Where available additional attributes such as volume estimate, date of occurrence, trigger, contributing factor, and reference to previous work are provided. </span></p> <p><span lang="EN-US">Most landslides in the database have been identified using Google Earth and publicly available lidar. Online mapping applications such as HazMapper by Scheip and Wegman (2021) and Arctic Landscape EXplorer (ALEX) by Lübker et al. (2024) have also been used to identify landslides based on the changes in multi-spectral indices derived from satellite acquired datasets. As most of the landslides have been identified using remote sensing techniques (optical, multi-spectral, lidar, InSAR), landslide type attribution should be considered preliminary, and no characterization of the current level of landslide activity or hazard are provided. The database spatial sampling biases includes detailed representation of areas with existing inventory and where lidar is available which allows for the identification of landslide features in forested terrain. Based on these limitations, the preliminary Canadian landslide database is appropriate for research projects and for use as part of the initial desktop review but should not solely relied on for formal landslide hazard assessments. </span></p> <p><span lang="EN-US">Version 12.0 includes the addition of 12,740 landslide features over version 11.0. Highlights of this version include the addition of Antoni Lewkowicz, (retrogressive thaw slumps from Canadian Arctic), Cory McGregor (landslides from Haida Gwaii and Akie River Valley, British Columbia), Jennifer Clarke (landslides from Okanagan and Shuswap, British Columbia), Aaron Steelquist (landslides from Fraser Canyon, British Columbia), and Caleb Ring (landslides from Fraser Canyon, British Columbia) as co-authors. This version also includes the addition of 694 new post-wildfire landslides by Carie-Ann Hancock along with updates (e.g. better constrained initiation date, location, landslide type) to 508 existing entries.</span></p> <p><span lang="EN-US">Version 12.0 standardizes the date format to YYYYMMDD. Effort was started and is still underway to standardize volume class categories (giant, large, medium, small) according to the classification proposed by McColl and Cook (2024). Point location and attribute data are provided as .csv file which can be imported in GIS software and as .kmz file for visualization using Google Earth. Summary statistics are provided in a separate spreadsheet. Summary statistics from previous versions are now provided in the different spreadsheet tabs. Release notes from this and previous versions are compiled in an accompanying pdf document.</span></p>
Spatio-temporal water surplus and evapotranspiration in the catchment area of the Vögelsberg landslide (Tyrol, Austria)
<p>Multi-temporal maps of daily water surplus and evapotranspiration in the catchment area of the Vögelsberg landslide (Tyrol, Austria) based on the SVAT model LWF-Brook90 from 01/01/2008 to 31/12/2019. The spatio-temporal results represent the hydrological forcing of acceleration phases of the deep-seated landslide (see also Pfeiffer et al. 2021, <a href="https://doi.org/10.1002/esp.5129">https://doi.org/10.1002/esp.5129</a>). To investigate the feasibility of a modified land cover as nature-based solutions to reduce the landslide's activity, three land cover scenarios were considered (under current climatic conditions):</p> <p>- Current land cover conditions classified based on air-borne laser scanning data</p> <p>- Forest scenario: catchment area completely covered by forests (hypothetical scenario)</p> <p>- Pole timber scenario: open land above agricultural areas is replaced by areas of pole timber (considered realistic)</p> <p>Three land cover classes (open land, pole timber, mature forest), 11 soil types and 5 vertical meteorological domains were distinguished. Maps were produced with a spatial resolution of 10m (Projection: Austria GK West, EPSG: 31254). The maps are provided as raster stacks in tif-format with each layer representing one day.</p> <p>For further details see OPERANDUM deliverables D4.5 and D4.6.</p>
Multi-temporal digital terrain models of the active deep-seated Vögelsberg landslide (OAL-Austria)
<p>Multi-temporal digital terrain models of the active deep-seated Vögelsberg landslide in OAL Austria (Lat: 47.272°, Lon: 11.597°) with a spatial resolution of 50cm. Raster were derived from classified 3D point clouds acquired with a Riegl VUX-1LR unmanned aerial vehicle laser scanner on August 3<sup>rd</sup> 2018, August 14<sup>th</sup> 2019 and November 6<sup>th</sup> 2020 (Projection: EPSG 31254). Ground classification after Axelsson (2000). Data was used to assess topographic changes at the toe of the active deep-seated Vögelsberg landslide in OAL-Austria.</p>
Hydrothermally altered landslide deposits at Askja, Iceland - field data
<p>We analyze the proximal deposits of the July 21, 2014 landslide at Askja (Iceland), by combining high-resolution imagery from Unoccupied Aircraft Systems (UAS) and in-situ hyperspectral field data. Results underline that the northern part of the landslide source region is a hydrothermally altered material class, which bifurcates halfway downslope and then extends to the lake. Here we provide the field data, such as the drone derived orthomosaic and digital elevation model (DEM), as well as hyperspectral field data. For locations of the data and measurements made we refer to the publication </p> <p>Pouria Marzban, Stefan Bredemeyer, Thomas R. Walter, Friederike Kaestner, Daniel Mueller and Sabine Chabrillat (2023) Hydrothermally altered landslide deposits at Askja, Iceland, identified by high resolution satellite and UAS imagery, spectral classification and hyperspectral field data.Frontiers in Earth Science Volcanology. Manuscript ID: 1083043</p>
Precipitation and fire history at landslide sites
<p>These data include precipitation and burned area histories for events listed in the NASA Global Landslide Catalog. Each landslide includes a location uncertainty estimate. Precipitation values are the mean of all values within the uncertainty radius, while the fraction burned is computed for burned area.</p> <p>These data are intended to be used with the an RMarkdown notebook available at <a href="http://doi.org/10.5281/zenodo.7653683">this GitHub repository</a></p>
Time-of-failure prediction of the Achoma landslide, Peru, from high frequency Planetscope satellites
<p><strong>Introduction</strong></p> <p>This repository contains the data used for the study of the slope instability of Achoma, Peru, described in Lacroix et al. (submitted). Specifically, the repository contains a time series of horizontal ground displacements, obtained from high frequency PlanetScope satellite between 2017 and 2020. It also contains two Digital Elevation Models, one from before the Achoma failure obtained with Pléaides stero images, and the other from just after the Achoma failure obtained with drone imagery.</p> <p>The data and methods used for the elaboration of this data repository are described in detail in Lacroix et al. (submitted). In this repository we also provide a short summary and overview of the data and methods used.</p> <p><strong>Data</strong></p> <p>A total of 79 PlanetScope scenes were used to produce the time series of horizontal horizontal ground displacements maps. Table 1 provides an overview of these data.</p> <p>Table1: Data used for the creation of this repository</p> <table> <tbody> <tr> <td> <p>Application</p> </td> <td> <p>Platforms</p> </td> <td> <p>Acquisition dates</p> </td> </tr> <tr> <td> <p>Pre-failure DEM</p> </td> <td> <p>Pléiades</p> <p> </p> </td> <td> <p>2017/05/13</p> </td> </tr> <tr> <td> <p>Post-failure DEM</p> </td> <td> <p>Drone</p> </td> <td> <p>2020/06/19</p> </td> </tr> <tr> <td> <p>Horizontal ground displacement</p> </td> <td> <p>PlanetScope</p> </td> <td> <p>79 scenes from 2017/11/27 to 2020/06/17</p> </td> </tr> </tbody> </table> <p><br> </p> <p><strong>Methods</strong></p> <p>The horizontal ground displacement maps, both along the NS and the EW directions (file names NSxxxxxxxx.tif and Ewxxxxxxxx.tif, where xxxxxxxx is the date in the format yyyymmdd) were created using the offset tracking methodology described in Bontemps et al. (2018), consisting of: (1) correlation of all the pairs of images using Mic-Mac (Rupnik et al., 2017), (2) masking the low correlation coefficient values (CC<0.7), (3) mosaicking correction, similar to stripe corrections (Bontemps et al., 2018), that we obtained by subtracting the median value of the stacked profile in the along-stripe direction, taking into account only stable areas, (4) least square inversion of the redundant system per pixel, weighted by the time separation between pairs (Bontemps et al., 2018), (5) correction of illumination effects (Lacroix et al., 2019), based on the 2 years of data between November 2017 and December 2019.</p> <p>The pre-failure DEM was computed from Ames Stereo Pipeline (Shean et al. 2016) and the methodology developed in (Lacroix, 2016) applied to the Pléiades stereo images (file name DEM_20170513_shifted_vertical2.tif ).</p> <p>The post-failure DEM was processed using the Structure from Motion-Multi View Stereo (SfM-MVS) methodology with the Agisoft Metashape Professional 1.5.5 software applied on 1824 pictures taken from the drone (file name Achoma_DEM_2020.06.20_UTM19S_50cm.tif ).<br> </p> <p><strong>Acknowledgements</strong></p> <p>P.L. acknowledge the support from the French Space Agency (CNES) through the TOSCA, PNTS, and ISIS programs.</p> <p><strong>Dataset attribution</strong></p> <p>This dataset is licensed under a Creative Commons CC BY 4.0 International License.</p> <p><strong>Dataset Citation</strong></p> <p>Lacroix, P., Huanca, J., Angel, L., Taipe, E.: Data Repository: Time-of-failure prediction of the Achoma landslide, Peru, from high frequency Planetscope satellites. Dataset distributed on Zenodo: 10.5281/zenodo.7866962</p>
Semi-automatic and manual shallow landslide inventories of two extreme rainfall events.
<p>This dataset contains the polygons of automatic ( PL) and manually (ML) based shallow landslides related to two extreme rainfall events. In KML format, the dataset can be visualized on GIS software or Google Earth.</p><p>With more details, it is possible to find:</p><ul><li>AOI_2016: The study area of the extreme rainfall of November 2016, Tanerello and Arroscia Valleys NW Italy.</li><li>The 2016_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in November 2016</li><li>The 2016_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in November 2016</li><li>AOI_2019_large: The study area of the extreme rainfall of October 2019 NW Italy.</li><li>AOI_2019: The testing area of the extreme rainfall of October 2019, Gavi Area NW Italy.</li><li>The 2019_PL_all: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (whole Study area)</li><li>The 2019_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (Gavi test area)</li><li>The 2019_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in October 2019</li></ul><p>GEE_Script: A list of codes used in Google Earth Engine to produce NDVI time series or averaged NDVI on some sample studied areas are reported in the attached PDF. The code may be pasted and copied to the Google Earth Engine console. </p><p>The codes (if an account on Google Earth Engine is active) may be reached directly from the following URLs: </p><p><strong>Script 1. </strong>NDVI time series of some sampled areas to select the best pair of images for the PL creation (Tanarello and Arroscia Valley and GAVI AOIs; Fig. 16 of the paper). Link to GEE: <a href="https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true">https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true</a></p><p><strong>Script 2.</strong> sampled NDVI time series from different intersection cases for the Tanarello and Arroscia Valley study area (2016 Event). Link to GEE: <a href="https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true">https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true</a></p><p><strong>Script 3. </strong>Sampled NDVI time series from different land-use cases for the Gavi study area (2019 Event). Link to GEE: <a href="https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true">https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true</a></p><p><strong>Script 4.</strong> Multi-temporal-averaged NDVIvar Link to GEE Script: <a href="https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true">https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true</a> for the whole Gavi study area (2019 flood) and <a href="https://code.earthengine.google.com/89e1c0a1361860cd407b7e6ab8bb95de?noload=true">https://code.earthengine.google.com/a3390b262cef1b5f42837c88d8791b5b?noload=true</a> for the entire Arroscia-Tanarello study area</p><p>The full description of the methodology can be found in the paper of Notti et al., 2023</p><p>Notti, D., Cignetti, M., Godone, D., and Giordan, D.: Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine, Nat. Hazards Earth Syst. Sci., 23, 2625–2648, <a href="https://doi.org/10.5194/nhess-23-2625-2023">https://doi.org/10.5194/nhess-23-2625-2023</a>, 2023</p>
Landslide inventory (1953-1996), Andrews Experimental Forest and Blue River Basin.
Landslide inventory (1953-1996), Andrews Experimental Forest and Blue River Basin. This layer is a combination of data from four landslide inventory efforts that have been conducted in the area beginning in the late 1960's. Data represent landslide occurrences between 1953 and 1996. Data are in the UTM coordinate system; zone 10, NAD27. The data table that describes the characteristics of the landslides inventoried in the following: Individual layers tied to inventories by Ted Dyrness, Fred Swanson, Dan Marion, and Matt Wallenstein were appended and linked to the shape file. This file is associated with the slideinv layer, which is a combination of data from four landslide inventory efforts that have been conducted in the area beginning in the late 1960's. Points have been screen digitized from field maps using streams, roads, contour lines, and harvest units as reference layers.
Images of Slow Moving Landslide in Panajachel Guatemala
<p>Images taken with a DJI Phantom 4 Pro in two different flight modes, safe and continuous, divided into two different .rar files. Included are the coordinates for the 4 ground control points, marked with white calc x's in the images, as well as the orthomosaic created with the images in Agisoft Metashape.</p> <p>The site is next to the highway 1 between Panajachel and San Andres Semetabaj in Solola Guatemala.</p> <p>Coordinate system used is UTM Zone 15N based on WGS84.</p> <p>The coordinates of points 2 - 5 are the four GCPs in the study area, with 5 being the take off and landing site. Point 1 represents the reference point approximately 1 km away, which was a previously placed permanent GCP. All points include the elevation ASL.</p> <p> </p> <p> </p>
Multi-temporal Landslide Inventory for the Far-Western region of Nepal
<p>The Multi-Temporal Landslide Inventory for the Far-Western region of Nepal datasets comprises 26350 different landslide events digitize in form of polygons from Google Earth satellite imagery interpretation. In Google earth has been used for interpretation 93 different sources for 79 different time slices between 2002 and 2018. The maximum scale of interpretation used is 1:1000, meanwhile the scale of digitalization was constant between 1:800 and 1:2000, resulting in a final visualization scale of 1:1000. All landslides in the inventory have been classified between deep-seated and shallow types (attribute field "Depth") by visual interpretation which have been later corroborated with calculations of the elevation differences within the surface of rupture area of the landslides</p> <p>The dataset comprises 4 different shapefiles:</p> <ul> <li><strong>"LandslideInventory_FarWesternNepal_Pol.shp"</strong>: Shapefile with 26350 Polygon features that bound completely the “zone of depletion” and partially the “zone of accumulation” of each identified landslide. Including completely the surface of rupture and more or less partially the depositional zone of the landslides. Landslide</li> <li><strong>"LandslideInventory_FarWesternNepal_Points.shp"</strong>: Shapefile with 25639 Point features that approximately correspond with the center of the surface of rupture area, the point location within each landslide has ben extracted automatically with GIS tools using ALOS PALSAR (12.5 m) DEM. </li> <li><strong>"LandslideInventory_FarWesternNepal_Points_Dated1992_2018.shp"</strong>: Shapefile with 8778 Point features for landslides in the inventory that have been dated within the period 1992-2018 (attribute field "Year". The dating of the landslides has been perform automatically by an own new toolbox in ArcGIS that compare annual Landsat (4-5, 7 and 8), to find sudden vegetation changes within the areas of the digitized landsldies. The tool has an accuracy of 83% to detect annual dates of activation or reactivations of the inventoried landslides. </li> <li><strong>"LandslideInventory_FarWesternNepal_AOI.shp"</strong>: Shapefile with the Polygon boundary of the landslide inventory Area of Interpretation.</li> </ul> <p>All shapefiles are in a UTM projected coordinate system UTM44N (WGS84).</p> <p> </p> <p>This research was funded by the UK Natural Environment Research Council (NERC) and Department for International Development (DFID) as project NE/P000452/1 (LandslideEVO) under the Science for Humanitarian Emergencies and Resilience (SHEAR) program.</p> <p> </p>
Precipitation-temporal-clustering-and-Italian-landslides
<p>This repository contains results about the spatial and temporal distribution of temporal clustering of precipitation and analysis of landslides triggers based on movement types over Italy.</p>
Rainfall-Induced Landslide Inventory of the 2014 Itaóca Event, Ribeira Valley, Brazil
<p>Heavy precipitation on January 12, 2014, triggered numerous shallow landslides in Itaóca, Ribeira Valley, Brazil. The inventory was manually created using a RapidEye satellite image (5 m spatial resolution) acquired on January 30, 2014, 18 days after the event. Specific criteria were applied to identify shallow landslides through image interpretation. These criteria included the absence of vegetation, proximity to the drainage network, altimetric variation, planar rupture surfaces, slope position, as well as the shape and size of the features. The dataset is provided in shapefile format and contains 1,723 polygons representing the areas affected by shallow landslides.</p> <p> </p>
Data and code for Decoding dynamic landslide hazard processes for a massive refugee camp (KTP) in Bangladesh
<p>The codes have been implemented using R 4.4.0. Landslide priority zonation using Monte Carlo simulation is implemented in Google Colab.</p> <p>A Dynamic Landslide Hazard Assessment has been conducted using a Generalized Additive Model (GAM). The results of the GAM are also compared with standard machine learning algorithms (MLs): NNET, RF, LDA, xgBoost, and SVM.</p> <p>The code is jointly developed by Dewan Haque and Ritu Roy, with collaboration from many others. The GAM code is an update from the study published by Zhice, F. (2023), <a href="https://doi.org/10.5281/zenodo.10395153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10395153</a>, adapted to apply it across settings. The ML code has been developed from scratch.</p> <p>The required data from intensive fieldwork and satellite image analysis is uploaded here to reproduce the results. Additionally, R Markdown files are provided.</p> <p>The ReadMe file here, as well as on GitHub, will be useful for further instructions.</p> <p>GitHub Link: https://github.com/Dewan-cpu/Decoding-Landslide-Hazard-Assessment</p>
Rapid mapping inventories of landslides triggered by heavy rainfall in the Emilia-Romagna region (Italy) during May 2023
<p>The dataset contains the spatial explicit inventories of landslides triggered by heavy rainfall in the Emilia-Romagna region (Italy) during May 2023. Hybrid pixel-based and object-based image analysis approach was used to generate the rapid mapping products. The fully automated supervised procedure relies on change detection analysis, based on quantitative variation of vegetation cover in pre-event and post-event remote sensing imagery. Two separate inventories were generated using:<br>- Copernicus Sentinel-2 MSI satellite imagery<br>- PlanetScope SuperDove satellite imagery</p> <p>The dataset is a spatial representation vector polygons representing potential landslides, distributed in GeoPackage format, complemented with confidence measure. It contains modified Copernicus Sentinel data, available at no cost from Copernicus Open Access Hub. Use of PlanetScope images is under license: ©Planet Labs PBC, CC BY-NC-SA 2.0.</p>
Large Landslide Exposure in Metropolitan Cities
<p>These datasets (.Rmd, .Rroj., .rds) are ready to use within the R software for statistical programming with the R Studio Graphical User Interface (https://posit.co/download/rstudio-desktop/). Please copy the folder structure into one single directory and follow the instructions given in the .Rmd file. Files and data are listed and described as follows:</p> <p>Main directory files: results_fpath</p> <ul> <li>Code containing statisticla analysis and ploting: 20240927_code.Rmd </li> <li>1_melted_lan_df.rds: Landslide time series database covering 1,085 landslides intersected with settlement footprints from 1985-2015.</li> <li>4_cities_lan.df.rds: City and landslide data for these 1,085 landslides intersected with settlement footprints from 1985-2015.</li> <li>7_zoib_nested_pop_pressure_model: brms statistical model file.</li> <li>ghs_stat_fua_comb.gpkg: Urban center data from the GHSL - Global Human Settlement Layer.</li> </ul> <p>Population estimation files: wpop_files </p> <ul> <li>2015_ls_pop.csv: Estimates of population on landslides using the 100x100 population density grid from the WorldPop dataset.</li> </ul> <p>Steepness and elevation analysis derived from SRTM and processed in Google Earth Engine for landslides, mountain regions and urban centers in cities: gee_files</p> <ul> <li>1_mr_met.csv: Elevation and mean slope for mountain region areas in cities</li> <li>2_uc_met.csv: Elevation and mean slope for urban centers (defined by in the GHSL data) in cities</li> </ul> <p>Standard deviation analysis derived from SRTM and processed in Google Earth Engine for mean slope in mountain regions and urban centers in cities: gee_sd</p> <ul> <li>gee_mr.csv: Mean slope and standard deviation for mountain region</li> <li>gee_uc.csv: Mean slope and standard deviation for urban centers (defined by in the GHSL data)</li> </ul> <p>Summary overview of cities with large landslides used in this study: cities_summary.csv</p> <p>Variables in this file are:</p> <ul> <li> <ul> <li>eFUA_name := City name</li> <li>cntry := Country name</li> <li>cntry_ISO := Country ISO code</li> <li>fua_area := Estimate of total metropolitan city area (km^2)</li> <li>ls_total_area := Total large landslide areas within city (m^2)</li> <li>ls_pct_fua_area := Mapped large landslides\n(% of the metropolitan city area)</li> <li>pop_FUA_2015 := Metropolitan population estimate (2015)</li> <li>pop_ls := Estimate of total number of people on large landslides</li> </ul> </li> </ul>
Data sets for assessing potential CC impacts on the activity of the Vögelsberg landslide
<p>Bias-corrected air temperature and precipitation time series (RCM sample from EURO CORDEX) for Kleinvolderberg station near the Vögelsberg landslide (OAL-AT) under RCP8.5, monthly water balance components derived from an empirical model for six elevation steps under current and potential land cover conditions for 1950-2100, median monthly displacement and current hydrological forcing of the Vögelsberg landslide</p>
On the estimation of landslide intensity, hazard and density via data-driven models.
<p>The geographic prediction of landslide occurrence is undertaken by assessing whether a slope may be stable or unstable. In other words, current practices treat slopes where a single landslide occurred in the same way as slopes where many landslides occurred. At the slope scale, this procedure inevitably underestimates the effect of multiple landslides.<br> Here we model the number of landslides per slope instead. Then, thanks to the close relation that the number of failures shows with respect to landslide size, we convert the estimated number of landslides into estimated landslide areas. Ultimately, we also estimate the expected proportion of a slope affected by landslides. This framework is more informative than the stable/unstable paradigm and may help landslide risk mitigation strategies.</p>
Event-based landslide susceptibility models (Styrian Basin, Austria)
<p><strong>Landslide susceptibility models</strong></p> <p>Landslide susceptibility modes are based on rainfall-triggered landslide events in the Styrian basin, Austria, in 2009 and 2014. Landslide susceptibility models are generalized additive models (GAM). <em>Note: Information on the exact location of landslides has been obscured.</em></p> <p><br> <strong>Uncertainty</strong></p> <p>Posterior simulations of the coefficients using a simple Metropolis Hastings sampler and a Gaussian approximation are available for GAM-Spatial and GAM-SM.</p> <p> </p> <p> </p> <table> <caption><strong>Overview of landslide susceptibility models</strong></caption> <thead> <tr> <th scope="col"><strong>GAM</strong></th> <th scope="col"><strong>Variables</strong></th> </tr> </thead> <tbody> <tr> <td>GAM-Co</td> <td>land surface variables, meteorological variables, geology, LULC</td> </tr> <tr> <td>GAM-SM</td> <td>GAM-Co, soil moisture</td> </tr> <tr> <td>GAM-SM+TC</td> <td>GAM-SM, five-day rainfall > 80 mm top-coded</td> </tr> <tr> <td>GAM-Spatial</td> <td>GAM-SM+TC, Gaussian process smoother</td> </tr> </tbody> </table> <p> </p>
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ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.