## 1 Introduction  
Catastrophic landslides, as profound natural calamities, exert ruinous impacts on property and claim lives across the world [1,2]. They result from various natural and human-induced factors, including heavy rainfall, snowmelt, earthquakes, fluctuating water levels, natural erosion, weathering processes, and human activities such as improper land use and engineering projects [3, 4, 5]. Landslides can severely impact infrastructure while also directly endangering human lives. [PERSON] and [PERSON] report that between 2004 and 2016, 4862 fatal landslides claimed up to 55,997 lives globally [6]. This alarming figure underscores the enormous threat that landslides pose to society. Furthermore, landslides exhibit remarkable spatial heterogeneity. The risk and frequency of landslides vary differently across regions. This geographical specificity makes the impact of landslide hazards on specific areas particularly significant. Therefore, it is imperative to engage in comprehensive research and to apply effective risk management strategies in areas characterized by high levels of risk. LSM (landslide susceptibility mapping) serves as a vital tool for disaster risk assessment, helping to identify vulnerable areas through detailed analysis and mapping, offering a strong scientific basis for informed decisions on disaster prevention and mitigation [7,8]. Recent advances in DL (deep learning) have demonstrated substantial potential for LSM, with CNNs (convolutional neural networks) emerging as particularly effective tools for capturing spatial patterns and complex correlations inherent in geospatial data [9,10]. [PERSON] [11] conducted a comprehensive evaluation of various CNN architectures for landslide susceptibility assessment, comparing their performance with traditional ML (machine learning) and DL techniques, and affirmed the reliability of CNN-based approaches in LSM. Despite their success, these models often encounter significant challenges, including overfitting, computational inefficiency, and limited generalization across diverse geographic regions. Although traditional CNN architectures are highly effective, they face difficulties in learning complex, hierarchical features, especially in the presence of limited labeled data or suboptimal data quality. To mitigate these issues, the present study utilizes ResNet18, a variant of the ResNet (residual network) architecture, which incorporates residual learning to facilitate the training of deeper networks while addressing the vanishing gradient problem. This distinctive feature of ResNet18 enables it to efficiently model intricate spatial dependencies in LSM tasks, even when faced with data sparsity or noisy inputs [12]. Furthermore, ResNet18's computational efficiency and its established success in a wide range of image-based tasks make it a highly suitable choice for LSM. In LSM, both predictive accuracy and the optimization of computational resources are paramount, and ResNet18 excels in addressing these requirements [13]. LSM has been a subject of significant interest in the academic community, evolving from early theoretical debates to its latest practical applications. It has evolved from a single analysis of natural factors to a comprehensive analysis of multiple factors, and its research methodology and technical methods have been constantly refined and developed. The data presented in Table 1 indicate that the field of landslide susceptibility research has expanded to the national and global scales. According to the analyses by [PERSON] [14] and [PERSON] [15] of large-scale and long-term landslide risk, the importance of conducting landslide studies at large spatial and temporal scales is evident. [PERSON]'s [16] research emphasizes that geological, geomorphological, and climatic processes (including disaster processes) are interrelated across regional and multi-temporal scales.