###### Abstract
Ancient landslides refer to landslides that occurred before the Holocene and have become relatively stable [24], and landslides with a volume of \(100\times 10^{4}\)-\(1000\times 10^{4}\) m\({}^{3}\) are classified as giant landslides [25]. But in this paper, all landslides that occurred before the obtained DEM years are considered \"ancient landslides\", where \"ancient landslides\" does not follow the definition of real ancient landslides in a strict temporal sense, and all landslides that are recognizable to the naked eye are considered \"giant landslides\", which also does not follow the definition of giant landslides in the strict spatial sense. We usually use susceptibility to evaluate the risk of landslides, which means we can select a few impact factors that are most relevant to the occurrence of landslides somewhere and then input both landslides and factors into a trained suitable machine learning model to predict the probability of landslides occurring [26; 27; 28; 29]. Nowadays, researchers continue to improve the accuracy of landslide occurrence probability by changing the landslide influence factors and innovating the training models. Although this type of method is relatively simple to operate and can predict a wider area at the same time, it also has some disadvantages; for example, different scholars may not choose exactly the same landslide occurrence indicators for training the model in the same area. Different scholars often have different division methods to divide the training data into certain intervals in order to reduce the redundancy of data. In addition, the important aspect of negative sample selection is handled differently by different scholars. In a word, the inconsistency of the above operations usually leads to incomplete consistent results. Therefore, we need to find an indicator directly related to the occurrence of landslides as much as possible and explore the relationship between landslides and this indicator so that we can use this indicator to indicate or predict the risk of landslides to some extent. So, we note the concept of excess topography, which was proposed in 2015 and refers to the volume of rock located on a threshold hillslope angle that is indicative of potentially unstable slopes. A study extracted excess topography in the Himalayan Karakoram range based on a 90 m DEM, and then the relationship between 492 obtained bedrock landslides (rockslides and rock avalanches) and excess topography was quantitatively analyzed at last [30]. Later, [PERSON] et al. 2021 extracted the excess topography of six watersheds in the Transverse Mountain region based on a 30 m DEM and quantitatively analyzed the relationship between the obtained data of 4430 large, medium and small landslide sites and the excess topography [31]. The studies of [PERSON] et al. and [PERSON] et al. both found that more than 70% of landslides were still distributed on excess topography when the threshold hillslope is 30\({}^{\circ}\). It is thus clear that most landslides occurred without changing the threshold hillslope, and excess topography can be used as an important indicator of actual landslides. In this research, we try to effectively identify GALs in a Lengqu River section based on 12.5 m DEM data by RRIM transformation and explore the potential risk location of GALs by quantitatively analyzing relationships between GALs, resurgent GALs, the deposition of inactive GALs and excess topography. This research is intended to provide a new reference basis for the investigation of potential risk locations of GALs. One of the most important innovations of this paper is to enhance the morphology, color and other features of GALs through two-dimensional visualization of changes in the topography using 12.5 m DEM data so that researchers can easily interpret landslides and to update the catalog of GALs in the Lengqu River section.