## 1 Introduction
[24] implemented a hierarchical method that refined intensity with mean shift segmentation and extraction of road centerlines with a Voronoi diagram. The proposed method improved both road extraction results and water surface detection. [PERSON] et al. [16] aimed to distinguish between road and ground points based on intensity constraints. In their proposed method, an improved skewness balancing algorithm was used for the calculation of the intensity threshold. There are deep learning techniques, such as PointNet [25] and PointConvNet [26], that take point clouds as direct input for object classification and segmentation problems. Following the gradual development of these approaches, PointNet++ was published for labeling the road points in the point cloud data [27]. There are, however, still issues to be addressed when relying solely on LiDAR data. For instance, there is no spectral information in LiDAR data which can lead to mixing between objects with similar density and texture such as parking lots. Therefore, the combination of satellite images and LiDAR data can resolve such deficiencies, leading to more accurate segmentation of the road network [22; 28; 29]. There have been numerous studies conducted on the potential benefits of remote sensing data in segmenting images. However, the combination and integration of different data sources are yet to achieve the same level of attention. [PERSON] et al. [30] conducted thorough research on integrating multiple sources and models and proposed an approach that combined LiDAR and multispectral images using different fusion strategies. They used an Infrared-Red-Green (IRRG) image and a combination of the Normalized Digital Surface Model (NDSM), Digital Surface Model (DSM), and Normalized Difference Vegetation Index (NDVI) obtained from LiDAR data. In a similar study, [PERSON] et al. [31] suggestedusing high-resolution images and nDSM derived from LiDAR. Their proposed method involves segmenting fused data, classifying image objects, generating a road network, and extracting the road centerline network using a multistage approach, including morphology thinning, Harris corner detection, and least square fitting. [PERSON] et al. (2019) introduced a novel method named the FuNet (Fusion Network) that integrates satellite images with binary road images generated from GPS data collection. This approach, which depends on a multi-scale feature fusion strategy, has been found to be more effective at resolving the road connectivity issue than using solely satellite imagery. [PERSON] and [PERSON] (2019), on the other hand, proposed a technique that involves combining hyperspectral images with LiDAR data for unsupervised segmentation. They applied a Gaussian filter to the point cloud while performing principle component analysis on the images, which yielded results used to create an affinity matrix. In another study, [PERSON] et al. (2019) presented multi-scale feature extraction for 3D road segmentation, combining characteristic features from high-resolution images and LiDAR data. Although there are findings suggesting the benefits of integrating diverse data sources for multiple applications, existing research indicates that there are still challenges that need to be addressed in this area. The combination of different types of remotely sensed data can be challenging, yet resourceful for segmentation studies considering the complexity of the solution. Specifically, the combination of 2D images and a 3D point cloud that has an irregular data structure requires complex solutions for deep learning architectures. An alternative and easier approach is to extract context information from the point cloud that represents the local 3D shapes and fuse them with the high-level features extracted from 2D optical images ([PERSON] et al., 2019).