## 1 Introduction  
Road collapse disasters constitute a prevalent natural hazard in mountainous and geologically complex regions, particularly in areas such as Guangxi, where the frequency of collapses is elevated due to intricate terrain, high precipitation, and fragile geological structures. These disasters pose substantial threats to transportation safety and regional economic development [1]. Annually, road collapses triggered by heavy rainfall and other contributing factors not only disrupt transportation networks and damage infrastructure but may also precipitate secondary disasters, such as debris flows, thereby impacting livelihoods and social stability [2]. Consequently, the effective assessment of road collapse disaster risk has emerged as a critical research focus in the domains of transportation safety and disaster management, providing a scientific foundation for disaster warning systems, emergency response strategies, and mitigation measures [3].  
Extensive research has been conducted both domestically and internationally on road collapse disaster risk assessment. Domestic studies predominantly employ traditional geological surveys and statistical analysis methods, constructing vulnerability assessment models based on multiple factors such as slope, precipitation, and soil type. While these methods have yielded some results in specific regions, their limitations have become increasingly apparent, especially when dealing with complex environmental factors and spatial data, leading to insufficient model accuracy and adaptability [4]. Moreover, with the development of remote sensing technology and geographic information systems (GIS), some domestic research has begun to integrate these technologies into disaster assessments to improve evaluation accuracy. However, challenges remain in multi-source data fusion, model scalability, and adaptability. In contrast, international research has widely adopted convolutional neural networks (CNNs) for disaster risk assessment. CNNs possess the ability to automatically extract features and effectively handle complex datasets, demonstrating exceptional performance in large-scale data analysis [5]. However, existing studies mostly focus on specific datasets or regions and are less adaptable to complex geological environments and data-scar areas, with the generalization capability of the models still requiring improvement [6].  
An analysis of domestic and international research indicates that although existing methods have achieved preliminary results in certain regions, there are still significant shortcomings in terms of adaptability and accuracy when faced with complex and dynamic environments and high-dimensional data [7; 8]. Therefore, this study proposes the following research hypothesis: the vulnerability of road collapse disasters is related to complex nonlinear relationships between multiple geological and environmental factors. CNNs can enhance disaster assessment accuracy by automatic feature extraction and multi-factor data fusion, especially in data-scarce or environmentally complex situations, where CNNs show strong adaptability [9].  
The main objective of this study is to construct a vulnerability risk assessment model for road collapse disasters based on CNNs, utilizing remote sensing imagery and GIS data to extract spatial features and improve disaster prediction accuracy and reliability through multi-factor fusion. Specifically, this paper will use geographic data and remote sensing imagery from Guangxi, combining factors such as slope, lithology, precipitation, and vegetation coverage, to build a risk assessment model for road collapse disasters and train and optimize the ResNet18 CNN model [5]. The research framework includes major steps such as data collection and preprocessing, CNN model construction, model training, and evaluation, with the goal of providing scientific support for the prevention and control of road collapse disasters [6].