## 1 Introduction  
Road segmentation is the process of predicting and classifying road pixels in an image to aid the generation of accurate road network information. Road segmentation using satellite images is a critical tool for efficient traffic management and planning. Proactive measures can be taken to identify potentially dangerous intersections and congestion areas, thereby reducing the risk of traffic-related accidents and improving the overall safety of a road network. Hence, authorities can use road segmentation to monitor changes over time and detect any obstacles or hazardous conditions that could make a route less safe, allowing for constructive action to be taken. Recently, advances in the field of artificial intelligence have resulted in various state-of-the-art model architectures and their optimizations to be proposed to solve complex segmentation problems [1; 2; 3]. Machine learning and deep learning approaches are being applied to improve the quality of road segmentation from remote sensing data [4; 5; 6; 7]. Naturally, a proper solution of road segmentation using remote sensing images is a highly non-linear problem; hence, deep learning-based semantic segmentation solutions are more favorable in this matter [8; 9; 10; 11]. Deep learning model development is seen as an epochal development. However, it is strongly data driven, which sometimes makes it difficult to make accurate predictions. The performance of deep learning models is greatly influenced by both the quality and quantity of the data utilized in their training process [12; 13]. If the data are not representative of the real-world scenario, this leads to poor performance. A major challenge is the shadow effect, which makes it difficult to segment the road pixels [14]. The resolution and spectral capability are highly interrelated, and they result in false perceptions at the boundary points of the road [15]. The resolution of the satellite image should give high spatial information for extraction. However, accessing high-resolution images can make it costly or impossible to perform qualified road segmentation. There are also a lot of problems with woodlands because of the lack of information about roads. The missing information can be completed using the LiDAR point cloud. Therefore, it may be possible to overcome the limitations of satellite images in areas where the road information is problematic and improve the accuracy of road segmentation by exploiting the additional geometric relations, integrating LiDAR data into deep learning models [16; 17]. LiDAR is seen as a valuable data source and it provides useful information that cannot be extracted from optical images. The LiDAR scanner can be mounted on terrestrial, mobile, and airborne platforms. The mobile and terrestrial LiDAR data can be used for the semantic segmentation of road information [18; 19], but the applications of these platforms are limited due to their small coverage areas and the difficulties of point cloud measurements increases in challenging areas [20]. To be able to conduct road segmentation in large areas, or even country-wide, the airborne LiDAR platforms become more feasible in this regard, as these platforms can cover cities and countries rapidly. There is substantial research on road segmentation using airborne LiDAR data. In [PERSON] et al. [21], using a grid index structure to detect roads in LiDAR point clouds, a morphological gradient was applied on ground points which were filtered using local intensity distribution. [PERSON] et al. [22] conducted a study based on similar parameters to the method of [PERSON] et al. [21]. After identifying the differences in shape, reflectance, and road width, road centrelines were extracted with local principle component analysis. Then, road networks were extracted using the global primitive grouping method. [PERSON] et al. [23] introduced a novel approach consisting of skewness balancing, rotating neighbourhoods, and hierarchical fusion and optimization to extract roads from point cloud data. [PERSON] et al.