## 5 Conclusions  
This research presents an economical solution, using machine learning with geospatial technology, to the classification of unlabeled slope structures using only the existing slope condition inventory (without need to carry out extensive fieldwork). The methodological process adopted here was designed sequentially to deliver insights into, or information about, the nature of unlabeled landslide inventory data. Thus, the results of each phase can be used as a standalone solution, or one can proceed with all phases for a higher degree of confidence (especially in cases where the inventory has a complex structure, or the study area is small with a semi-uniform surface). Two machine learning algorithms were investigated with a multiclass inventory: first, nonlinear clustering t-SNE, which preserves as much of the significant structure of the high-dimensional data as possible in a low-dimensional map; and second, Apriori data mining to find the common rules of association between the inventory and the antecedent factors. These algorithms revealed, with acceptable degrees of confidence, the nature of the unlabeled data. However, without a validation step, significant doubt, arising from uncertainty in the data and the models, remains. Moreover, the rules of association for vulnerable slopes and no-landslide conditions were not effective in landslide or unlabeled slope identification, confirming a passive prediction capacity identified through a previous  
Figure 10: Validation results using automatic change detection results. study in landslide susceptibility modeling in similar study area [4; 28]. Therefore, an automatic change detection technique was used with actual optical images to verify the findings. Eventually, the objective was achieved with multiple validation tests and well-designed integrated methodology. Without needing extensive fieldworks, and using open data and an open coding environment, the presented approach will help with updating of any natural hazard's incident inventory (medium or regional scale study area), as well as inventory that has different slope failures or slope conditions (creeps, unstable, etc.). Furthermore, this is especially useful in scarce data environments. Formal analysis, conceptualization, methodology, software: O.F.A. ; data curation, conceptualization: I.-T.H. and Y.P. ; writing--original draft preparation, review and editing; O.F.A., I.-T.H., A.A., Y.-K.L. ; resource visualization, investigation: H.-J.P.; supervision, project administration, lab facilities, computers and hardware, research allowances: M.-S.L., S.-W.K. All authors have read and agreed to the published version of the manuscript. This research was supported by Space Core Technology Development Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT (2018M1A3A3A02066002), and MSIT (Ministry of Science, ICT), Korea, under the High-Potential Individuals Global Training Program (2019-01561) supervised by the IITP (Institute for Information & Communications Technology Planning and Evaluation). We express our gratitude to the Scientists's Adoption Academy research network (scadacademy.com) to facilitate the research group meetings and results sharing. Not applicable. [PERSON]; [PERSON]; [PERSON]; [PERSON] Application of a two-step cluster analysis and the Apriori algorithm to classify the deformation states of two typical colluvial landslides in the Three Gorges, China. _Environ. Earth Sci._**2016**, _75_, 1-16. [CrossRef]
* [PERSON] et al. (2014) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] A new method to determine eroded areas in arid environment using Landsat satellite imagery. In Proceedings of the IOP Conference Series: Earth and Environmental Science, Jakarta, Indonesia, 23-24 January 2014. * [PERSON] (2006) [PERSON] Methods for Investigation and Analysis of Landslides on Natural Terrain in Korea. Ph.D. Thesis, Andong National University, Andong, Korea, 2006.