## 4 Conclusions  
In this study, a feature-wise fusion strategy of optical images and point cloud was performed to enhance the road segmentation performance of a deep learning model based on Deep Residual U-Net architecture. In order to compensate for the missing information in optical satellite images that stems from the obstacles and shadow effects in the scenery, we proposed to combine 2D and 3D information to compensate for the absence of depth information in satellite images. For this purpose, high-resolution satellite images and their corresponding road masks, generated over Florida state, were combined with 3D geometric features computed from airborne LiDAR data. In the proposed fusion technique, the optical satellite images were fed into U-Net-based model architecture to generate deep features. Before the final convolution layer, these high-level features were concatenated with the geometric features of the point cloud. The experimental results validated the effectiveness of the proposed fusion approach for enhancing road segmentation. The combination of 2D and 3D information from satellite images and LiDAR point cloud showed superior results in areas where the satellite image-only models could not predict the road pixels accurately. In challenging areas where the road pixels precluded observation, such as wooded areas and shadows from objects in scenery, the deep learning models trained with the fusion approach predicted the road pixels and the continuity of the road network better than deep learning models trained with satellite images only. The study provided new insight into the relationship between the 2D and 3D features of satellite images and LiDAR data. Moreover, the findings of this study showed the combination of data from various sources can be promising for enhancing the quality of road segmentation. The importance of the features showed that while the optical images have a significant impact on the prediction of road pixels, the contribution of LiDAR features, specifically linearity and height difference within neighbouring points, can lead to a more effective segmentation model development for the extraction of the road network. These 3D features and the geometric relation between the neighbouring points are proven to be significant as the optical images and indicate the importance of contextual information among the point cloud data. It is necessary to further investigate the optimal integration of the most important LiDAR features with the optical satellite images so as to not only capture the geometric properties of the road network efficiently but also to minimize computation costs. It is worth noting that the advancement in the remote sensing data collection techniques and mobility of measuring platforms for LiDAR and laser scanning can ease the generation of accurate and reliable point cloud data. Hence, with these technological developments, the combination of 2D and 3D information from the road networks to increase the performance of deep learning models can be a feasible solution for road extraction studies over challenging areas. The aim of this study is not to exceed the performance of all existing models, but rather to show the combination of optical images and LiDAR can exceed the performance of satellite-only segmentation models. The improved model showed superiority over the problematic areas. In future studies, multi-model fusion strategies will be analyzed to exploit the contribution of LiDAR features that were found to be most effective in road network extraction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Input Data**} & \multirow{2}{*}{**Backbone**} & \multicolumn{5}{c}{**Metrics**} \\ \cline{3-6}  & & **Precision** & **Recall** & **F1-Score** & **IoU** & **Kappa** \\ \hline Image & & 0.862 & 0.809 & 0.830 & 0.732 & 0.810 \\ \hline Image + LiDAR & ResNet152 & 0.880 & 0.852 & 0.863 & 0.781 & 0.843 \\ \hline
**Selected features** & & **0.865** & **0.845** & **0.847** & **0.752** & **0.824** \\ \hline \hline \end{tabular}
\end{table}