## 1 Introduction  
Natural disasters such as earthquakes, floods, hurricanes, and landslides often cause significant damage to critical infrastructure, including road networks [1]. Roads play a vital role in disaster response logistics, serving as essential corridors for search and rescue operations, relief supply delivery, and post-disaster recovery activities. However, transportation infrastructure is particularly vulnerable during disasters as access is frequently obstructed by debris, landslides, or collapsed structures [2].  
Traditional road damage assessment methods primarily rely on field surveys or the manual interpretation of aerial imagery. While effective in small-scale areas, these approaches are labor-intensive, time-consuming, and potentially hazardous for field personnel [3,4]. In large-scale disaster zones, it is nearly impossible to conduct exhaustive on-site inspections within a short period. These limitations underscore the urgent need for an automated and scalable approach to road damage assessment.  
Recent advancements in remote sensing and the increasing availability of high-resolution satellite imagery have opened new possibilities for rapid disaster assessment [5, 6, 7]. Satellite images can provide timely, wide-area coverage immediately after a disaster, enabling situational awareness without requiring physical access to the site [8, 9, 10, 11]. When combined with deep learning techniques, such imagery can facilitate the automated detection and quantification of infrastructure damage [12, 13], thereby accelerating decision-making in disaster response and recovery planning. However, achieving reliable automated assessment is a formidable technical challenge. The visual signatures of road damage can be subtle, and more importantly, the severe class imbalance--where damaged areas constitute a tiny fraction of the total area--poses a significant hurdle to robust model training [5, 9].  
Most existing deep learning-based research in this domain has focused on building damage assessment [14, 15, 16, 17, 18, 19, 20], which is supported by datasets such as xBD and xView2 [21, 22, 23, 24, 25]. In contrast, despite the critical operational role of road networks during emergencies, studies targeting road infrastructure remain limited [26, 27]. The lack of reliable road damage detection and evaluation techniques introduces a significant gap in current disaster response systems.  
To address this issue, this study aims to propose a deep learning framework that automatically detects and classifies road damage using pre- and post-disaster satellite imagery. Unlike building-focused approaches, the proposed method segments road infrastructure into three categories--background, undamaged, and damaged roads--allowing for more refined and timely infrastructure assessments. The proposed model leverages change detection techniques to capture structural variations in road segments and provides quantitative information that can support disaster response activities, such as prioritizing road restoration and estimating accessible routes.  
The remainder of this study is organized as follows: Section 2 provides an informative summary of the related works. Section 3 describes the research methodology with the underlying concepts and algorithms for road damage detection, which is followed by experimental designs with specific processes and parameters in Section 4. Lastly, Sections 5 and 6 contain quantitative and qualitative results of three approaches and conclusions, research limitations, and future research issues.