## 2 Related Work
However, the geospatial elements taken into consideration tend to have homogenous hyperspectral signatures and to be grouped into clearly defined regions covering smaller areas, unlike secondary road networks, which are far more complex in nature. Most of the methods proposed to extract road structures from remote sensing imagery can be fitted into two categories: extraction of the road surface or extraction of road edges or centrelines, and use of supervised learning to extract the geometries using their radiometric, spatial and photometric features. These techniques have also been successfully applied to tasks related to road detection such as infrastructure monitoring or vehicle navigation [20]. Many of the existing road extraction studies use traditional machine learning and image segmentation methods. [PERSON] et al. [21] tackle the secondary road extraction task by applying pre-processing with Gaussian filters to smooth and remove noise from the input and to enhance features specific to roads. As post-processing, they apply Hough transform to detect discontinuous boundaries of regions based on the global features of a single orthoimage of 7000 \(\times\) 6000 pixels in size. In [22], the task is formulated as a Markov random field inference parameterized in terms of the location of the road edges and centrelines and makes use of OpenStreetMap to build an urban scene dataset covering 1.5 km\({}^{2}\); the model is trained using structured Support Vector Machines (SVMs). One obvious disadvantage of applying traditional machine learning techniques is represented by the need to analytically and mathematically model and understand the relations between the input and the expected predictions. The representations selected for training can result in models performing well on small datasets, but may not be suitable for solving complex tasks that involve a large amount of data. To address the geospatial complexity of the road network, we chose the deep learning approach, which enables the building of robust segmentation models with a higher generalization capability that are more suitable for modelling complex functions as those needed to describe features specific to roads. Apart from this, the main drawback found in relevant works is related to the reduced study areas taken into account (as also pointed out in [23]). Most existing works focus on small selected study areas, with favourable scenarios [24], which may not be representative for a large-scale extraction of these geospatial elements. [PERSON] et al. [25] propose a model consisting of five convolution layers, replacing the Fully Connected layers with Global Average Pooling (GAP) layers to extract road and building geometries from 50 aerial images with resolutions of \(1500\times 1500\) pixels. They extract spatial features from adjacent pixels to enhance the predictions and reach performance metrics as high as 81.6%. [PERSON] et al. [26] propose a FCN-based architecture called UFCN (U-shaped FCN), based on stacking convolutions and their mirrored \"deconvolutions\", and train it on a limited dataset containing 76 images of roads subjected to real-time data augmentation. [PERSON] et al. [27] recognize the complexity of the road extraction task when state-of-the-art networks obtained levels of precision of a maximum 72% on a single uncropped aerial orthophoto, even by applying filtering, data augmentation and post-processing operations. We believe that training the networks on small datasets can lead to inconsistent predictions, due to the lack of representative samples and lack of variety in data. The solution described in this paper addresses this drawback by building a new dataset composed of aerial orthoimages divided into tiles of \(256\times 256\) pixels covering extended areas of the Spanish territory. We have also found that existing works generally apply concepts from popular architectures, while adding small tweaks (e.g., adding batch normalization after ReLU activations). For example, inspired by deep residual learning and U-Net, [PERSON] et al.