The area of Poland where the low relief area exceeds 80 % of the country's territory and results in various morphogenetic processes was selected for the analysis. Thus, the morphogenetic diversity of the plains is not reflected in the existing classification systems. The development of the automatic classification of landforms mainly focuses on landforms related to the fluvial morphogenetic cycle. Low-relief areas are not fully the main subject of geomorphometric analyses. This dataset can be used as a proxy and is expected to contribute to the modeling and estimation of various points that are known to be related to topography. In addition, the results of k-means clustering using slope gradient, HAND, and surface texture, which can be joined with the dataset as a simple terrain classification, are also available. This dataset contains the calculated terrain measurements (slope gradient, HAND, surface texture, local convexity, Sinks) and polygon areas as attributes, as well as the ID number of the MERIT-Basins’ unit catchment. We created a global polygon dataset of the shapefile format divided into uniform slopes from slope gradients and HAND (height above the nearest drainage) calculated using the 90m spatial resolution MERIT DEM, and combined this data with the unit catchments of MERIT-Basins. However, due to the resolution of the DEMs used, the terrain classification data from previous studies could not discriminate small landforms, such as narrow valley bottom plains, and small-rises within the plains. Global terrain classification data have been used for various issues that are known to be related to topography, such as estimation of soil types, estimation of Vs30, and creation of seismic hazard maps. The datasets of this article are available at. This dataset can be used as a proxy and is expected to contribute to modeling and estimation in various applications that are known to be related to topography. This dataset showed improvements in terms of capturing the small rises in plains compared to the authors' previous global terrain classification data. The 15 clusters were prepared to observe the outline of the terrain without any processing, whereas the 40 clusters were prepared to group and reclassify the polygons to create zoning data for each region. The clustering results were prepared in 15 and 40 global uniform clusters and 15 and 40 clusters for each basin to understand the global appearance of the terrain and provide zoning data for regional problem-solving. In addition, we performed k-means clustering on the dataset using slope gradient, HAND, and surface texture, which can be combined with the dataset as a simple terrain classification. To address this problem, we first merged regions with similar topographic characteristics using slope gradients and HAND (height above the nearest drainage) calculated by the 90-m-spatial-resolution DEMs interpolated from the multi-error-removed improved-terrain DEM (MERIT DEM), and united the polygons with the unit catchments of the MERIT-Basins dataset, so that the polygons contain calculated terrain measurements (slope gradient, HAND, surface texture, local convexity, sinks) and noise types as attributes, as well as the ID number of the unit catchment. Owing to the greater regional variation of small landforms, there is trade-off between DEMs of higher resolution and the creation of global geomorphological legends. However, due to the resolution of digital elevation models (DEMs), the terrain classification data from previous studies could not discriminate small landforms such as plains at the bottom of narrow valleys and small rises in plains. Global terrain classification data have been used for various issues related to topography such as the estimation of soil types and of ground vulnerability to earthquakes and the creation of seismic hazard maps. (2021) conducted a terrain classification for the whole of Japan using a 30-m DEM, aiming to achieve a terrain classification that reflected the vulnerability of the ground for various slopes from alluvial plains to mountainous areas, and that had both geomorphological and geotechnical classifications without major contradictions. 2011) as an additional parameter for the extraction of small rises in plains, as well as methods of topographic measurement in which DEM unevenness or noise is not amplified. (2021) introduced HAND (Height Above the Nearest Drainage Rennó et al. To accommodate the increasing resolution of DEMs and to produce more practical data, Iwahashi et al. The use of higher-resolution DEMs was necessary to improve on this performance, but it was thought that automatic classification would be more difficult with higher-resolution DEMs because artificial steep cliffs in heavily man-made altered plains or unevenness of accuracy would become more apparent.
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