## 1 Introduction  
Complete records of previous slope failures, their distribution, and slope condition, are among the best data for predicting the location of future failures. However, the inventory record needs, to the extent possible, to include complete and specific information on triggering factors, amount, type, area, volume, and date of incidents [1]. Slope activities, in any specific study area, have unique type and spatial distribution patterns that are determined by the prevailing antecedent (conditioning) factors. Specifically, the distribution of shallow landslides, a typical land degradation, tends to be spatially clustered and to reflect the distribution and structure of the slope material [2, 3, 4]. Topographic factors, such as slope condition, vegetation cover, soil type, and other land covers, tend to have classes or values common to each specific landslide type. Knowledge of the specific slope formation conditions at incident locations allows identification, and spatial classification of (previously unlabeled) slope formations with similar values of the antecedent factors. To achieve such spatial pattern visualization, dimensionality reduction machine learning, with a t-distributed stochastic neighbor embedding (t-SNE) algorithm, offers a promising and practical capacity to analyze the structure of unlabeled slopes, leading to pattern identification and matching to known locations with similar formations [5]. t-SNE has been used to visualize high-dimensional datasets, such as remote sensing products and open data, by the clustered embedding of high-dimensional data into lower-dimensional space, such as a two- or three-dimensional map [6; 7; 8]. In addition, t-SNE is regarded as a very efficient technique for detecting potential errors in a reference dataset through visual analysis of a t-SNE plot [9]. Moreover, t-SNE returns highly compressed data, making it suitable for identification of large margins within a dataset. However, t-SNE may be unsuitable for datasets with recurring step-like temporal profiles [6]. The Apriori algorithm, based on association rule learning (ARL), is often used to identify frequently occurring item sets (associations) within a dataset [10]. It operates on large databases through several iterations based on a priori knowledge [11]. The Apriori algorithm has been widely employed to determine association rules in nonlinear modeling problems, and successfully applied to not only determine states of landslide deformation but also predict landslide movements [12; 13]. Use of machine learning to assess associations among antecedents has become user friendly, especially with the adoption of the simple, but effective, Apriori association rules function. To verify associations between landslide locations, we need evidence of actual conditions based on prior images or supporting reports. Semiautomatic techniques, including supervised classification-based change detection and manual digitization, may be used to extract landslides from satellite images [7]. More effective, in terms of time and effort, is the automatic detection of landslides from satellite images using unsupervised classification-based change detection [8]. [PERSON] et al. [8] developed an automatic technique to detect landslide scars in the Jinsha River area of China. The developed technique was based on k-means classification of the Normalized Difference Vegetation Index (NDVI) time series derived from Sentinel-2 data. [PERSON] and [PERSON] [9] reported that variations in vegetation phenology over time affect the spectral responses of vegetation in tropical seasonal biomes, which can affect change detection applications. By contrast, [PERSON] et al. [14] confirmed that lithology change detection can be useful for identification of eroded areas. Therefore, in this study, a new automatic technique, based on lithology change detection, was used to detect landslides that occurred during/after intense rainfall events in Pohang state, Southwestern Korea.