Spatial datasets for benchmarking machine learning-based landslide susceptibility modelsMendeley Data
This article presents a comprehensive dataset developed for benchmarking machine learning-based landslide susceptibility models. The dataset includes landslide polygons delineated through manual interpretation of high-resolution satellite imagery and controlling factors data extracted from topograph...
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Elsevier
2024-12-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S235234092401117X |
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| author | Guruh Samodra Mukhamad Ngainul Malawani Indranova Suhendro Djati Mardiatno |
| author_facet | Guruh Samodra Mukhamad Ngainul Malawani Indranova Suhendro Djati Mardiatno |
| author_sort | Guruh Samodra |
| collection | DOAJ |
| description | This article presents a comprehensive dataset developed for benchmarking machine learning-based landslide susceptibility models. The dataset includes landslide polygons delineated through manual interpretation of high-resolution satellite imagery and controlling factors data extracted from topographic maps and Indonesia's national digital elevation model (DEMNAS). Landslide events were mapped by comparing pre- and post-event satellite imagery from Tropical Cyclone (TC) Cempaka, which occurred from 27 to 29 November 2017, and verified through field surveys. Pre-event landslides were mapped using Google Earth imagery, while post-event landslides were mapped using Pleiades Pan-sharpened Multispectral Natural Color Band imagery sourced from the European Space Agency (ESA) via Indonesia's National Institute of Aeronautics and Space (LAPAN). The landslide polygons identify areas with confirmed landslide activity, while the controlling factors dataset includes topographic attributes such as slope, aspect, elevation, profile curvature, plan curvature, terrain wetness index, stream power index, land use, distance to road, and distance to river. The dataset is publicly available and aims to promote transparency, reproducibility, and collaboration in landslide research. It offers significant reuse potential for researchers across diverse domains and regions, enabling comparative studies, model benchmarking, and validation efforts. This dataset provides a valuable resource for advancing machine learning applications in landslide susceptibility modeling and supporting a wide range of geospatial analyses. |
| format | Article |
| id | doaj-art-72f4b8df6d3c47929c61aed5fb0e69c2 |
| institution | DOAJ |
| issn | 2352-3409 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-72f4b8df6d3c47929c61aed5fb0e69c22025-08-20T02:51:45ZengElsevierData in Brief2352-34092024-12-015711115510.1016/j.dib.2024.111155Spatial datasets for benchmarking machine learning-based landslide susceptibility modelsMendeley DataGuruh Samodra0Mukhamad Ngainul Malawani1Indranova Suhendro2Djati Mardiatno3Corresponding author.; Department of Environmental Geography, Faculty of Geography, Universitas Gadjah Mada, 55281, IndonesiaDepartment of Environmental Geography, Faculty of Geography, Universitas Gadjah Mada, 55281, IndonesiaDepartment of Environmental Geography, Faculty of Geography, Universitas Gadjah Mada, 55281, IndonesiaDepartment of Environmental Geography, Faculty of Geography, Universitas Gadjah Mada, 55281, IndonesiaThis article presents a comprehensive dataset developed for benchmarking machine learning-based landslide susceptibility models. The dataset includes landslide polygons delineated through manual interpretation of high-resolution satellite imagery and controlling factors data extracted from topographic maps and Indonesia's national digital elevation model (DEMNAS). Landslide events were mapped by comparing pre- and post-event satellite imagery from Tropical Cyclone (TC) Cempaka, which occurred from 27 to 29 November 2017, and verified through field surveys. Pre-event landslides were mapped using Google Earth imagery, while post-event landslides were mapped using Pleiades Pan-sharpened Multispectral Natural Color Band imagery sourced from the European Space Agency (ESA) via Indonesia's National Institute of Aeronautics and Space (LAPAN). The landslide polygons identify areas with confirmed landslide activity, while the controlling factors dataset includes topographic attributes such as slope, aspect, elevation, profile curvature, plan curvature, terrain wetness index, stream power index, land use, distance to road, and distance to river. The dataset is publicly available and aims to promote transparency, reproducibility, and collaboration in landslide research. It offers significant reuse potential for researchers across diverse domains and regions, enabling comparative studies, model benchmarking, and validation efforts. This dataset provides a valuable resource for advancing machine learning applications in landslide susceptibility modeling and supporting a wide range of geospatial analyses.http://www.sciencedirect.com/science/article/pii/S235234092401117XLandslide area dataLandslide point dataConditioning factors dataControlling factors dataPixelRaster |
| spellingShingle | Guruh Samodra Mukhamad Ngainul Malawani Indranova Suhendro Djati Mardiatno Spatial datasets for benchmarking machine learning-based landslide susceptibility modelsMendeley Data Data in Brief Landslide area data Landslide point data Conditioning factors data Controlling factors data Pixel Raster |
| title | Spatial datasets for benchmarking machine learning-based landslide susceptibility modelsMendeley Data |
| title_full | Spatial datasets for benchmarking machine learning-based landslide susceptibility modelsMendeley Data |
| title_fullStr | Spatial datasets for benchmarking machine learning-based landslide susceptibility modelsMendeley Data |
| title_full_unstemmed | Spatial datasets for benchmarking machine learning-based landslide susceptibility modelsMendeley Data |
| title_short | Spatial datasets for benchmarking machine learning-based landslide susceptibility modelsMendeley Data |
| title_sort | spatial datasets for benchmarking machine learning based landslide susceptibility modelsmendeley data |
| topic | Landslide area data Landslide point data Conditioning factors data Controlling factors data Pixel Raster |
| url | http://www.sciencedirect.com/science/article/pii/S235234092401117X |
| work_keys_str_mv | AT guruhsamodra spatialdatasetsforbenchmarkingmachinelearningbasedlandslidesusceptibilitymodelsmendeleydata AT mukhamadngainulmalawani spatialdatasetsforbenchmarkingmachinelearningbasedlandslidesusceptibilitymodelsmendeleydata AT indranovasuhendro spatialdatasetsforbenchmarkingmachinelearningbasedlandslidesusceptibilitymodelsmendeleydata AT djatimardiatno spatialdatasetsforbenchmarkingmachinelearningbasedlandslidesusceptibilitymodelsmendeleydata |