## 7 Conclusions  
We made steps towards reducing the tedious and time-consuming task of obtaining road geometries from aerial imagery data by proposing a large-scale road extraction solution based on semantic segmentation methods. We trained the proposed hybrid configurations on a dataset containing aerial information and ground-truth road masks from representative areas of the Spanish territory and obtained maximum IoU and F1 scores of 0.5790 and 0.7120, respectively, and a minimum loss of 0.4985 by applying feature mapping techniques. The best performing hybrid model (with U-Net as segmentation architecture with SEResNeXt50 as backbone network) achieved gains in performance metrics in the order of 3.5% when compared to the original architecture trained from scratch.  
The results obtained are promising, but we identified various factors that influenced the model's predictive behaviour, such as obstructions and occlusions in the scenes or other geospatial elements with similar hyperspectral properties (e.g., dry riverbeds, railroads or irrigation canals). We have found that sometimes the model failed to segment all roads within an area, or would omit connection segments or would classify pixels to the wrong class. These limitations can be addressed with post-processing operations, as the web platform allows easy integration of additional processing algorithms. We believe that the proposed solution will improve the utility of the aerial imagery projects for automatic mapping of secondary roads, and can provide a technical base for large-scale road mapping by assigning pixel information with geographical information.  
The next step is to further increase the size of the dataset with samples from challenging areas and apply post-processing techniques based on generative learning, this way obtaining more robust models trained with varied data, capable of a more efficient extraction of the road surface areas. We also believe that an ideal solution requires a good balance between machine-generated features and manual mapping, and we will continue to improve the platform in order to make the task process smoother by designing an extension to solve incidents by an operator, this way introducing humans into the decision-making process.  
In this study, Conceptualization, C.-I.C., R.A. and M.-A.M.-C.; Data curation, C.-I.C. and R.A. and M.-A.M.-C.; Formal analysis, C.-I.C., R.A. and M.-A.M.-C.; Funding acquisition, M.-A.M.-C. and F.S.; Investigation, C.-I.C., R.A. and M.-A.M.-C.; Methodology, C.-I.C., R.A. and M.-A.M.-C.; Project administration, R.A., M.-A.M.-C. and F.S.; Resources, R.A., M.-A.M.-C. and F.S.; Supervision, R.A., M.-A.M.-C. and F.S.; Validation, C.-I.C., R.A., M.A.M.C. and F.S.; Visualization C.-I.C., R.A., and M.-A.M.-C.; Writing--original draft, C.-I.C., R.A. and M.-A.M.-C.; Writing--review & editing, C.-I.C., R.A., M.-A.M.-C. and F.S. All authors have read and agreed to the published version of the manuscript.  
This research received funding from the Cartobot project, in collaboration with Instituto Geografico Nacional (IGN), Spain.  
We thank [PERSON] and all other Cartobot participants for their help in the initial phases of the research and in generating the dataset.  
The authors declare no conflict of interest.