With the development of artificial intelligence techniques for geographical knowledge discovery, simulated terrain generation based on deep-learning algorithms has become one practical way to construct accurate terrain data. However, it is still necessary to discuss whether the simulated topographic data contain the characteristics of specific landforms and can support related geographical studies. Therefore, in this study, a deep learning-based model inspired by previous research is constructed to generate loess landform data. We analyzed the influence of inputting different topographic features on terrain generation and evaluated the similarity between the simulated and reference data. The results show that the deep learning-based model can generate simulated topographic data that include similar elevation and slope probability distributions to the reference data of the loess landform. In addition, the generated results may have inaccurate terrain details, which can be regarded as noise in some cases. This indicates that the selection of input features should be carefully considered. Finally, the simulated data can subsequently support landform and terrain research, especially with intelligence algorithms that require large sets of topographic data.

The Loess Plateau is a region of importance in geomorphologic research because of its typical loess layers and intense surface erosion. Analysing the landforms on the Loess Plateau is helpful for understanding changes in the surface environment. However, geomorphologic data with high resolution are lacking for the Loess Plateau, which limits the progress of geomorphologic studies at finer scales. This study provides the first 30 m resolution landform classification and characteristics dataset for the Loess Plateau (LPL30). Considering morphological characteristics, dominant dynamics, and material conditions, the landforms on the Loess Plateau were categorized into three levels with 28 landform types. Moreover, we calculated 6 metrics to quantify the spatial distribution and category composition of the landforms in 16 analytical units with different scales and shapes, resulting in 96 geomorphologic descriptors. This dataset provides fundamental data for the study of landform formation processes and evolution mechanisms at various scales and can be employed as a geomorphological gradient to support ecology and environmental research.