## 2 Related Work
[28] purpose ResUnet for road extraction by semantic segmentation on the Massachusetts roads dataset, replacing the convolutional blocks with residual modules. They obtained a decrease in parameters of \(\frac{3}{4}\), while increasing the performance metrics by 1% when compared to the original U-Net. [PERSON] et al. [29] propose the RoadNet framework, composed of a modified version of VGGNet architecture, to analyse and predict the surfaces, edges, and centrelines of roads in urban scenes. Similar to other FCN architectures, the CNNs are concatenated and use receptive fields of \(1\times 1\) pixels in size, obtaining F1 Scores of maximum 94%. The F1 score indicates the balance between correctness (Precision, \(P\)) and completeness (Recall, \(R\)) using the confusion matrix, F1 = \(2\times[(P\times R)/(P+R)]\). Correctness (\(P\)) is the fraction of the pixels correctly labelled as roads and the total number of road pixels, \(P\) = \(TP/(TP+FP)\), while completeness (\(R\)) is the fraction of all correctly labelled road pixels and the total number of correctly labelled pixels, \(R\) = \(TP/(TP+FN)\)[30]. Similarly to the IoU score, the F1 score ranges from 0 (worst value) to 1 (best score). [PERSON] et al. [31] built a semantic segmentation model for autonomous driving applications with VGGNet as the backbone and trained it to extract road boundaries using street scenes tagged with their semantic contour. The authors of [32] modify SegNet [33] by incorporating an Exponential Linear Unit (ELU) activation function and add conditional random fields (CRFs) for sharpening the road predictions. In this paper, we propose fifteen hybrid segmentation models based on state of-the-art networks and apply transfer learning techniques to increase their generalization capacity. Researchers also incorporated global and local attention procedures into neural network architectures as an attempt to consider positional information when working with larger remotely sensed areas. One such example is GL-Dense-U-Net, proposed by [PERSON] et al. [34], which uses DenseNet [35] for the contracting part and U-Net's symmetry to extract the local and global information through Attention Units, inspired by pyramid feature maps. The model obtained an F1 Score of 0.9516 on a dataset containing 224 Google Earth images. Similarly, [PERSON] et al. [36] take a FCN-like approach to extract features from the last convolutional layer at multiple scales and encode them into global image features through feature coding techniques. In [37], this task is approached from a temporal perspective by modifying SegNet to extract roads from low resolution videos captured by Unmanned AerialVehicles (UAVs). The model uses feature maps from the corresponding encoder stage to upsample and adds an iterative algorithm to process the temporal dimension. In our view, adding attention procedures to deep learning models can be considered a disadvantage when working at larger scales due to the additional computational effort required. We overcame this scenario by building a web platform architecture capable of managing the information related to the absolute position of the tiles. In this work, we address the above-mentioned challenges found in the existing literature by proposing a unitary extraction solution based on hybrid semantic segmentation models trained with aerial orthoimagery covering extended representative areas of the Spanish territory. The solution can be considered a complete workflow for road surface extraction from aerial orthoimagery and employs a web platform capable of dealing with the evaluation of larger areas and the joining of segmentation predictions in an efficient manner.