###### Abstract  
Landslide susceptibility assessment is critical for hazard mitigation and land-use planning. This study evaluates the impact of two different non-landslide sampling methods--random sampling and sampling constrained by the Global Landslide Hazard Map (GLHM)--on the performance of various machine learning and deep learning models, including Naive Bayes (NB), Support Vector Machine (SVM), SVM-Random Forest hybrid (SVM-RF), and XGBoost. The study area is a 2 km buffer zone along the Duku Highway in Xinjiang, China, with 102 landslide and 102 non-landslide points extracted by aforementioned sampling methods. Models were tested using ROC curves and non-parametric significance tests based on 20 repetitions of 5-fold spatial cross-validation data. GLHM sampling consistently improved AUROC and accuracy across all models (e.g., AUROC gains: NB +8.44, SVM +7.11, SVM-RF +3.45, XGBoost +3.04; accuracy gains: NB +11.30%, SVM +8.33%, SVM-RF +7.40%, XGBoost +8.31%). XGBoost delivered the best performance under both sampling strategies, reaching 94.61% AUROC and 84.30% accuracy with GLHM sampling. SHAP analysis showed that GLHM sampling stabilized feature importance rankings, highlighting STI, TWI, and NDVI as the main controlling factors for landslides in the study area. These results highlight the importance of hazard-informed sampling to enhance landslide susceptibility modeling accuracy and interpretability. landslide susceptibility assessment; SHAP method; machine learning; deep learning 2020 IEEE International Conference on Robotics and Automation (ICRA), Vol., pp. 2020-2020, 20  
occur. Landslide susceptibility mapping has proven to be a highly useful tool in landslide management over the years. The approaches employed to conduct landslide susceptibility mapping can be divided into three main categories: inventory mapping, knowledge-based models, and data-driven models [5,6]. Prior to the maturity of GIS technology, the assessment was mainly conducted using inventory mapping. The accuracy of the knowledge-based models, including Analytical Hierarchy Process (AHP) and Weighted Overlay Analysis, reflected on the experts' knowledge regarding the landslide mechanisms, thus lacking objectivity. The studies based on data-driven models have proliferated in recent years with booming machine learning techniques and more accessible public aerial imagery databases. In landslide susceptibility assessment, machine learning applications are predominantly focused on supervised learning approaches [7], with the most widely adopted models including logistic regression (LR), support vector machines (SVM) [8, 9, 10], random forest (RF) [9,11], convolutional neural network (CNN) [12], deep belief network [13], and recurrent neural network (RNN) [8,12]. Developing a universally applicable landslide susceptibility model is challenging, for it has been observed that the weights of the contributing indicators showed some consistency across models within a single AOI, but significant variability across multiple AOIs [14]. Also, the predictive performance of a specific model can vary under entirely different geological and geomorphological conditions. In light of this, the primary goal of this research is to identify the most suitable model for landslide susceptibility assessment within G3033 Duku Expressway buffer zones. This research aims to examine a range of approaches, from basic models to hybrid methods and more advanced techniques, while also assessing the impact of different sampling strategies on model performance. The findings of this research are expected to contribute not only to improving landslide susceptibility modeling within road buffer zones but also to providing valuable insights for landslide mitigation throughout the entire lifecycle of road construction projects. Although machine learning techniques have been widely applied in landslide susceptibility modeling [15], the influence of non-landslide sampling strategies on model performance and robustness remains underexplored [11,16].