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227 results for “landslides”
Triggers and consequences of landslide-induced tsunamis – reconstruction of the Taan Fiord 2015 tsunami event
<p>Models codes are provided for some examples of the adopted modeling approaches.</p> <p>STL. files for the solid bodies are available in the zip file.</p> <p>A bathymetric map of the Taan Fiord on October 2015 and related contour shape file are provided.</p> <p>Values of the solid-fluid mixture density for different initial close packing volume fractions are shown in the table.</p> <p>A table summarizes the available model approaches, implemented in FLow 3D, useful in reproducing a Landslide-induced tsunami</p>
Dataset for "Mapping Water Flow Pathways in the Fengjiaping Landslide Using Self-Potential and Electrical Resistivity Tomography"
<p>This dataset includes soil temperature, moisture, and electrical conductivity measurements taken at a depth of approximately 50 cm, as well as the digital elevation model, electrical resistivity tomography, and self-potential data used in the manuscript "Mapping Water Flow Pathways in the Fengjiaping Landslide Using Self-Potential and Electrical Resistivity Tomography" submitted to <em>Comptes Rendus Geoscience</em>.</p>
India Landslide Susceptibility Map ILSM (100m resolution)
<p>Landslide susceptibility represents the potential of slope failure for given geoenvironmental conditions. The existing landslide susceptibility maps suffer from several limitations, such as being based on limited data, heuristic methodologies, low spatial resolution, and small areas of interest. In this study, we overcome these limitations by developing a probabilistic framework that combines imbalance handling and ensemble machine learning for landslide susceptibility mapping. We employ a combination of one-side sampling and Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE) to eliminate class imbalance and develop smaller representative data from big data for model training. A blending ensemble approach using hyperparameter tuned Artificial Neural Networks, Random Forests, and Support Vector Machine, is employed to reduce the uncertainty associated with a single model. The methodology provides the landslide susceptibility probability and a landslide susceptibility class. A thorough evaluation of the framework is performed using receiver operating characteristic curves, confusion matrices, and the derivatives of confusion matrices. This framework is used to develop India's first national-scale machine learning based landslide susceptibility map. The landslide database is carefully curated from global and local inventories, and the landslide conditioning factors are selected from a multitude of geophysical and climatological variables. The Indian Landslide Susceptibility Map (ILSM) is developed at a resolution of 0.001o (~100 m) and is classified into five classes: very low, low, medium, high, and very high. We report an accuracy of 95.73%, sensitivity of 97.08%, and matthews correlation coefficient (MCC) of 0.915 on test data, demonstrating the accuracy, robustness, and generalizability of the framework for landslide identification. The model classified 4.75% area in India as very highly susceptible to landslides and detected new landslide susceptible zones in the Eastern Ghats, hitherto unreported in the government landslide records. The ILSM is expected to aid policymaking in disaster risk reduction and developing landslide prediction models. </p>
Landslide Influencing Factors for Landslide Susceptibility Mapping in Lombardy, Italy
<p>A selection of Landslide Influencing Factors used for Landslide susceptibility mapping through machine learning models covering the Lombardy region in Italy.</p> <p>A list of the factors:</p> <ul> <li>Averaged Hourly Precipitation for the year of 2020. Source: Precipitation data from ARPA Lombardia.</li> <li>Digital Elevation Model [meters], 5m/pix. Source: GeoPortale Lombardia.</li> <li>Eastness [unitless], 5m/pix. Source: DTM derived.</li> <li>Faults buffer (50, 100, 200, 500, >500), [meters], 1:10 000. Source: GeoPortale Lombardia, derived.</li> <li>Land Use Land Cover Map. Source: GeoPortale Lombardia.</li> <li>Lithology, 5m/pix. Source: GeoPortale Lombardia.</li> <li>Normalized Difference Vegetation Index, [unitless], 10m/pix. Source: Sentinel-2 derived for the year 2020.</li> <li>Northness [unitless], 5m/pix. Source: DTM derived.</li> <li>Orientation of the slope faces [degrees], 5m/pix. Source: DTM derived.</li> <li>Plan Curvature [unitless], 5m/pix. Source: DTM derived.</li> <li>Profile Curvature [unitless], 5m/pix. Source: DTM derived.</li> <li>River buffer (50, 100, 200, 500, >500), [meters], 1:10 000. Source: GeoPortale Lombardia, derived.</li> <li>Road buffer (50, 100, 200, 500, >500), [meters], 1:10 000. Source: GeoPortale Lombardia, derived.</li> <li>The 90<sup>th</sup> percentile of hourly precipitation for the year of 2020. Source: Precipitation data from ARPA Lombardia.</li> <li>Topographic Wetness Index, 5m/pix . Source: DTM derived</li> </ul> <p>The maps are in WGS 84/ UTM zone 32N (EPSG:32632).</p> <p>The map production process details are discussed in Xu et al. 2024. If you use the dataset, please, cite also the paper:</p> <p><em>Qiongjie Xu, Vasil Yordanov, Lorenzo Amici & Maria Antonia Brovelli (2024) Landslide susceptibility mapping using ensemble machine learning methods: a case</em><br><em>study in Lombardy, Northern Italy, International Journal of Digital Earth, 17:1, 2346263, DOI:10.1080/17538947.2024.2346263</em></p> <p>The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.</p> <p>The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001.</p> <p> </p>
CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection
<p>In this work, we present the CAS Landslide Dataset, a large-scale and multisensor dataset for deep learning-based landslide detection, developed by the Artificial Intelligence Group at the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS). The dataset aims to address the challenges encountered in landslide recognition. With the increase in landslide occurrences due to climate change and earthquakes, there is a growing need for a precise and comprehensive dataset to support fast and efficient landslide recognition. In contrast to existing datasets with dataset size, coverage, sensor type and resolution limitations, the CAS Landslide Dataset comprises 20,865 images, integrating satellite and unmanned aerial vehicle data from nine regions. To ensure reliability and applicability, we establish a robust methodology to evaluate the dataset quality. We propose the use of the Landslide Dataset as a benchmark for the construction of landslide identification models and to facilitate the development of deep learning techniques. Researchers can leverage this dataset to obtain enhanced prediction, monitoring, and analysis capabilities, thereby advancing automated landslide detection.</p> <p>If you use our data, please cite our work published in Scientific Data.</p> <p>Xu, Y., Ouyang, C., Xu, Q. <em>et al.</em> CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection. <em>Sci Data</em> <strong>11</strong>, 12 (2024). https://doi.org/10.1038/s41597-023-02847-z</p>
Landslide rainfall-induced Inventory at Japan case study (2017-2020)
<p>This dataset is used in the article, which title is "Combination of optical images and SAR images for detecting landslide scars, using a classification and regression tree", published in the International Journal of Remote Sensing (https://doi.org/10.1080/01431161.2023.2224096).</p><p>The dataset includes historical landslide inventories conducted by the Geospatial Information Authority of Japan (GSI) and digitized itself.</p>
Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be
<p>Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be</p>
Satellite Image Maps and GIS File of the Masara, Maco, Davao de Oro Landslide
<p>These maps and GIS file provide visualizations of the landslide that occurred in Brgy. Masara, Maco, Davao de Oro, Philippines on 06 February 2024.<br><br>The PlanetScope Super Dove satellite image was acquired four (4) days after the landslide event (10 February 2024). Using the satellite image, the extents of the landslide were manually delineated using GIS software. We estimated that the larger landslide has a surface area of approximately 85,960 sq. m., while the smaller one measures approximately 759 sq. m. Unfortunately, some portions of the image near the landslide area are contaminated by cloud shadows, making it challenging to visualize and accurately map the extent of the landslide.<br><br>Also included is a high-resolution satellite image depicting Brgy. Masara before the occurrence of the landslide. The image was acquired sometime in November 2019. The manually delineated extent of the landslide area (from the PlanetScope image) is overlaid to easily visualize the impact of the landslide on the nearby community.<br><br>Credits:<br><br>* GIS analysis and map preparation: Jojene R. Santillan<br>* Imagery © 2024 Planet Labs Inc., Maxar<br><br>Access to the PlanetScope image was made possible by the Planet Education and Research Program.<br><br>****<br>Disclaimers:</p> <p>The maps and GIS files ("Products") shall be used for non-commercial, reference purposes only. They shall not be used as replacements to authoritative maps and information provided/issued by mandated government agencies.</p> <p>Accuracy and Limitations: The accuracies of the Products are dependent on the source datasets, and limitations of the software and algorithms used and implemented procedures. The Products are provided “as is” without any warranty of any kind, expressed or implied. CCGeo does not warrant that the Products will be complete, and meet the needs or expectations of the user, or that the operations or use of the maps and GIS files will be error-free.</p> <p><br>Limitation of Liability: CCGeo and Caraga State University will not be held liable for any incidental, consequential, special, exemplary, or indirect damages (including lost profits and lost data) arising from or relating to the use of the maps and GIS files.</p>
Supplemental Material for "Anomalous Shear Stress Variation in Wet Granular Medium: Implications for Landslide Lateral Faults"
<p>Dataset and Constrained Parameters for "<strong><em>Anomalous Shear Stress Variation in Wet Granular Medium: Implications for Landslide Lateral Faults</em></strong>" by Chang et al.</p> <p>This dataset comprises 64 rheological experiments, which have been encapsulated in four experimental groups: Full Height, Half Height, Half Height & Load, and Ethanol-Water. </p> <p>The experiments were conducted using a double-cylinder geometry on three-phase granular media to simulate the lateral faults of slow-moving landslides. The dataset includes Mechanical Data for time-evolution measurements of shear stress, fluid volume fraction under varying conditions, and Image Data for velocity fields derived from PIV (Particle Image Velocimetry) analysis.</p> <p>In addition, it provides constrained parameters, such as steady-state shear stress and the characteristics of the flow structure.</p> <p>Article information:</p> <div> <div>Chang, C., Ohno, K., Schulz, W. H., & Yamaguchi, T. (2025). Anomalous Shear Stress Variation in Wet Granular Medium: Implications for Landslide Lateral Faults. <em>Geophysical Research Letters</em>, <em>52</em>(7), e2024GL113816. <a href="https://doi.org/10.1029/2024GL113816">https://doi.org/10.1029/2024GL113816</a></div> </div>
Landslide Susceptibility Mapping using Machine Learning: Details of articles published in 2009-2021
<p>To understand the trend of landslide susceptibility mapping using machine learning, we prepared a detailed list of 140 recently published articles. The Harzing desktop application was used to get the published articles by searching for "landslide susceptibility machine learning" keywords in the "Title" field of the application and searched using Google Scholar within the Harzing application. The search returned a total of 172 articles published from 2009 to 2021, and after eliminating papers that could not be accessed, we had 140 useable published articles. The articles also included some recently published articles which are pre-print and not peer-reviewed.</p>
Landslide location, surface roughness, and age for the Teanaway basin, USA
<p>Excel database of mapped landslides in the Teanaway basin, Washington State, US. Landslides were mapped from 1m lidar provided by Quantum Spatial and publicly available at https://lidarportal.dnr.wa.gov/. The surface roughness was calculated with the MAD metric available at https://github.com/cageo/Trevisani-2015. </p>
Inventory of landslides triggered by the 2015 Mw 6.0 Sabah earthquake (Malaysia)
<p>These files are related to the paper “Landslides triggered by the 2015 Mw 6.0 Sabah (Malaysia) earthquake: inventory and ESI-07 intensity assignment” by Ferrario M.F., submitted to NHESS</p> <ul> <li>Shapefile of the mapped landslides and study area</li> <li>Spreadsheet with data on inventories of earthquake-triggered landslides</li> </ul>
Dataset of landslide susceptibility in north-east India
<p>Dataset of input data and results of the landslide susceptibility assessment in north-east India presented in the article titled: Mapping susceptibility with open-source tools: a new plugin for QGIS.</p>
Mechanisms of the Non-uniform Breach Morphology Evolution of Landslide Dams Composed of Unconsolidated Sediments During Overtopping Failure
<p>Data that can be used to replicate and validate the results presented here are available through the upload file.</p> <p>Data access will be available upon request prior to official publication of this work.</p>
HR-GLDD: A globally distributed high resolution landslide dataset
<p> HR-GLDD, a high-resolution (HR) dataset for landslide mapping composed of landslide instances from ten different physiographical regions globally: South and South-East Asia, East Asia, South America, and Central America. The dataset contains five rainfall triggered and five earthquake-triggered multiple landslide events that occurred in varying geomorphological and topographical regions.</p>
Rainfall threshold data for landslides in the Three Gorges reservoir area
<p>Contains selected data on landslide susceptibility and rainfall thresholds in the Three Gorges reservoir area.</p>
Supplementary Data: Human settlement pressure drives slow-moving landslide exposure
<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 folders are described in the README.md file</p> <p>0_ferrer_etal_2024.Rproj</p> <p>README.md</p> <p>1_R_Notebook:</p> <ul> <li>SlowMovingLandslide_Exposure.rmd</li> <li>Ferrer2024_FloodExposureAnalysis.Rmd</li> </ul> <p>2_data:</p> <ul> <li>1_input: <ul> <li>sm_database_2023_01_17.csv</li> <li>sm_database_references.pdf</li> <li>gmba_v2</li> <li>ipcc_regions</li> <li>natrual_earth</li> </ul> </li> <li>2_processed: <ul> <li>model_data_2023_01_17.csv</li> <li>summary_database.csv</li> </ul> </li> </ul> <p>3_results:</p> <ul> <li>1_model <ul> <li>2023_12_31.model.rds</li> </ul> </li> <li>2_figures: <ul> <li>main_figure_elements</li> </ul> </li> <li>3_ext_figures</li> </ul> <p>4_manuscript_figures:</p> <ul> <li>1_main figures</li> <li>2_extended_figures</li> </ul> <p><br>5_floding_analysis:</p> <ul> <li>flood_comp_mod.csv</li> <li>flood_comp_model.rds</li> </ul>
GNSS Ground Monitoring Data of the Hongyanzi Landslide Area in China from November 11, 2021 to June 4, 2022
<p>This dataset comprises Global Navigation Satellite System (GNSS) ground monitoring data collected from five distinct locations within the Hongyanzi landslide area in China. The data spans a critical time period from November 11, 2021, to June 4, 2022, providing valuable insights into the temporal deformation behavior of the landslide.</p> <p>To enhance the applicability and comparability of these GNSS measurements, we have processed the raw three-dimensional (3D) displacement vectors and projected them onto the Line-Of-Sight (LOS) direction of Interferometric Synthetic Aperture Radar (InSAR) observations. This transformation facilitates direct comparisons between the GNSS-derived deformation rates and those derived from InSAR data, enabling a more comprehensive understanding of the landslide dynamics.</p> <p>The dataset includes:</p> <ul> <li><strong>Time series of 1D displacement measurements</strong> along the InSAR LOS direction for each of the five GNSS stations.</li> </ul> <p>The high-resolution temporal coverage of this dataset, coupled with the transformation to the InSAR LOS direction, makes it an invaluable resource for researchers investigating the geomechanics of the Hongyanzi landslide, as well as for validation and calibration of remote sensing-based deformation monitoring techniques.</p> <p><strong>Potential Uses</strong>:</p> <ul> <li>Validation of InSAR-derived deformation maps and velocity fields.</li> <li>Study of landslide kinematics and triggering mechanisms.</li> <li>Integration with other geophysical and geological datasets for comprehensive hazard assessments.</li> <li>Development and testing of advanced deformation monitoring and early warning systems.</li> </ul> <p><strong>Access and Citation</strong>:<br>This dataset is freely available for research purposes under the <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener noreferrer">Creative Commons Attribution 4.0 International License</a>. Please cite this dataset using the following reference (to be filled in upon upload completion):</p> <p>Aoqing Guo, 2024. GNSS Ground Monitoring Data for the Hongyanzi Landslide Area in China: Transformed to InSAR LOS Direction (November 11, 2021 - June 4, 2022). Zenodo. 10.5281/zenodo.13254357.</p>
Inventory of coal mining caused landslides and fissures in the center Chinese Loess Plateau
<p>A new inventory of coal mining caused landslides and fissures in the center of the Chinese Loess Plateau was generated based on four high-resolution DOMs conducted by UAV surveys and field investigations from 2020 to 2023. The field named "area" in the attribute tables of shapefiles named "2020_Landslides", "2021_Landslides", "2022_Landslides", and "2023_Landslides" indicates the area of the landslides (square meter) , and the "Time" indicates the occurred year of landslides. The field named "Length" in the attribute tables of shapefiles named "2020_Fissures", "2021_Fissures", "2022_Fissures", and "2023_Fissures" indicates the length of the fissures (meter), and the "Time" indicates the occurred year of fissures. The inventory of landslides and fissures in 2020 published by Yang et al., (2022) was referenced when we conducted visual interpretation.</p>
The complementary distribution of coseismic and slow-moving landslides along the Longmenshan fault zone
<p><a href="../api/records/13143195/draft/files/JZG.zip/content" target="_blank" rel="noopener noreferrer">JZG.zip</a>, The inventory of coseismic landlide for Jiuzhaigou earthquake. </p> <p><a href="../api/records/13143195/draft/files/Surface%20rupture.zip/content" target="_blank" rel="noopener noreferrer">Surface rupture.zip</a> The surface rupture of Wenchuan erthquake</p> <p><a href="../api/records/13143195/draft/files/detected%20slow-moving%20landslide_6660.txt/content" target="_blank" rel="noopener noreferrer">detected slow-moving landslide_6660.txt</a> The detected slow-moving landslide using Sentinel-1 images by deployed phase gradient stacking technique.</p> <p><a href="../api/records/13143195/draft/files/Snow_polygon.zip/content" target="_blank" rel="noopener noreferrer">Snow_polygon.zip</a> The snow-covered polygon in the study area</p> <p><a href="../api/records/13143195/draft/files/ROI-1_ALOS_phase%20gradient.zip/content" target="_blank" rel="noopener noreferrer">ROI-1_ALOS_phase gradient.zip</a> the stacked phase gradient files generated from ALOS-1 images and flagged slow-moving landslide manually for ROI-1</p> <p><a href="../api/records/13143195/draft/files/ROI-1_ALOS_phase%20gradient.zip/content" target="_blank" rel="noopener noreferrer">ROI-2_ALOS_phase gradient.zip</a> the stacked phase gradient files generated from ALOS-1 images and flagged slow-moving landslide manually for ROI-2</p> <p><a href="../api/records/13143195/draft/files/Lushan_ALOS_phase%20gradient.zip/content" target="_blank" rel="noopener noreferrer">Lushan_ALOS_phase gradient.zip</a> the stacked phase gradient files generated from ALOS-1 images and flagged slow-moving landslide manually in the focused region for affected area of Lushan earthquake.</p> <p><a href="../api/records/13143195/draft/files/rivers.zip/content" target="_blank" rel="noopener noreferrer">rivers.zip</a> the rivers in the study area</p> <p><a href="../api/records/13143195/draft/files/rough1.tif/content" target="_blank" rel="noopener noreferrer">rough1.tif</a> the terrain roughness for the study area</p> <p><a href="../api/records/13143195/draft/files/rough1.tif/content" target="_blank" rel="noopener noreferrer">slope1.tif</a> the terrain slope for the study area</p> <p><a href="../api/records/13143195/draft/files/A30m_5km_topo1.tif/content" target="_blank" rel="noopener noreferrer">A30m_5km_topo1.tif</a> the terrain relief for the study area</p> <p><a href="../api/records/13143195/draft/files/grd%20S1_DT62.zip/content" target="_blank" rel="noopener noreferrer">grd S1_DT62.zip</a> part of .grd files of stacked phase gradient for descending track 62 of Sentinel-1 images which covering the plotted regions in the supplementary material</p> <p><a href="../api/records/13143195/draft/files/grd%20S1_AT128.zip/content" target="_blank" rel="noopener noreferrer">grd S1_AT128.zip</a> part of .grd files of stacked phase gradient for ascending track 128 of Sentinel-1 images which covering the plotted regions in the supplementary material</p> <p><a href="../api/records/13143195/draft/files/grd%20S1_AT55.zip/content" target="_blank" rel="noopener noreferrer">grd S1_AT55.zip</a> part of .grd files of stacked phase gradient for ascending track 55 of Sentinel-1 images which covering the northeast section of surface rupture that generated in the 2008 wenchuan earthquake.</p> <p><a href="../api/records/13143195/draft/files/grd_Jiuzhaigou_before_after%20earthquake.zip/content" target="_blank" rel="noopener noreferrer">grd_Jiuzhaigou_before_after earthquake.zip</a> the .grd files of stacked phase gradient for ascending track 128 of Sentinel-1 images which covering the affected regions before and after Jiuzhaigou earthquake.</p> <p> </p> <p> </p> <p> </p>
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