## 3 Results & Discussion
### Discussions  
Regarding the improvements in areas where pixel complexity increases, the models trained with the fusion strategy showed better performance compared to image-only solutions, as highlighted with yellow boxes in Figure 5. The contribution of the fusion strategy can be readily observed by examining individual results and visualized road predictions. It can be seen that the fusion helped to complete the road geometry in these areas. The model is able to further segment the road pixels in the presence of tree cover more successfully than when using only satellite images in the deep learning model. The precision value for the individual predictions, where the image-only model exhibits worse performance, is increased up to 5% using the fusion strategy. This indicates that adding LiDAR features increased the number of true positives obtained. Using the ResNet-152, the highest F1-Score (93.7%) was achieved and the lowest (77.3%) when applying the ResNet-18. Road boundary continuity was achieved for the woodlands shown in Figure 5 and was able to increase up to 3% of recall values. To clarify the superiority of the proposed approach, predictions in wooded areas where the road pixels are blocked by trees are presented in Figure 6. Moreover, LiDAR-image fusion increased the performance of prediction in areas where there are shadow effects in the satellite images (see Figure 7). The recall and the F1-score values are significantly higher in these images. Additionally, the shadow factor can be seen to be too complex when relying only on satellite data. The proposed approach is effective at providing reinforcing information. Another problem related to road integrity at circular intersections has been resolved. This study demonstrates that the proposed strategy was perfectly executed in terms of both the quality and quantity of road extraction. Compared to the recall and IoU values, improved GAN approaches, such as those introduced by [PERSON] et al. [15] and [PERSON] et al. [43], have demonstrated similar recall and IoU results using only satellite images. Studies utilizing the Vaihingen and Potsdam datasets published by the International Society for Photogrammetry and Remote Sensing (ISPRS) are particularly prominent when studying multiple data sources. These datasets consist of Digital Aerial Images and Digital Surface Models, which classify various features such as surfaces and buildings. To benchmark their studies against previous studies, the researchers evaluated the accuracy of estimating impermeable surfaces formed by roads in these datasets. Their findings indicate that the use of fusion techniques led to an increase in the F1-Score statistics by 1% in the Vaihingen dataset and 2% in the Potsdam dataset when compared to the original U-Net model with ResNet 50 integration, as reported in previous studies by [PERSON] et al. [29] and [PERSON] et al. [30]. The study utilized models 5-6, which yielded similar results. Furthermore, it was found that integrating GPS data into satellite images improved relative accuracy by 2% [PERSON] et al. [32], and analysis of the tested models supports this observation in this study. In addition, this study revealed that roads were more completely represented in individual image analysis, a significant finding previously reported in other studies, particularly in woodland and shadow areas. As noted by [PERSON] et al. [31], while this study has achieved road connectivity completion even on roads with curves, post-processing techniques can be employed to further enhance the quality of results on such roads. The effectiveness of the fusion strategy approach can be demonstrated by calculating feature importance. In Figure 8, the relative importance of each feature is presented. The optical satellite images were found to be one of the most relevant features, as expected, with 23% importance. The model architecture seems to benefit the most from the change in absolute heights within neighbourhoods, i.e., height difference, and the linearity out of 13 LiDAR features.