## 1 Introduction  
According to statistics from the Ministry of Development (Spanish: \"Ministerio de Fomento\") [1], in 2019 the total mileage of roads in Spain was 655,322 km, of which 165,624 km (25%) are paved roads (including 17,021 km of routes of great capacity like highways). The remaining 499,698 km (75%) constitute the secondary road network (Spanish: \"Red secundaria de carreetas\"). Being able to obtain an accurate representation and distribution of these roads would be useful for government agencies, helping in the monitoring and detection of changes in order to provide up-to-date cartography. At the moment, at the national level, the road mapping operation is a challenging and time-consuming manual process, even with open access to high-resolution remote sensing imagery. The advances in machine vision started with the development of Convolutional Neural Network (CNN) architectures and incentivized the emergence of modern semantic segmentation techniques [2; 3; 4]. We believe that these advancements can help in achieving an efficient large-scale extraction of the surface area of secondary roads. However, we have to take into account the great complexity of this task. These roads have different spectral characteristics due to the various kinds of material used (asphalt, cement, gravel, etc.) and are often covered by obstructions present in the scenes (e.g., dense vegetation). Furthermore, these structures are complex in nature, due to the absence of clearly defined edges, the differences in widths, and the large curvature changes which can complicate the extraction of detailed information. In this paper, we tackle the automation of the road mapping task by using openly available high-resolution aerial orthoimagery and propose an extraction solution based on semantic segmentation. For this, we explore a wide range of hybrid segmentation architectures to identify the most suitable one for this application and our dataset, and propose a novel approach by implementing the best performing model on a web application capable of analysing extended areas, and joining adjacent tiles before converting the output into road vectors compatible with geospatial databases. To the best of our knowledge, this is the first intent of a large-scale road extraction system integrated on a web platform designed to eliminate the need for computationally expensive attention procedures. This deep learning-based solution can successfully analyse large areas, predict whether each pixel of the input aerial imagery belongs to a secondary road and overlap the predictions, and represents a complete unitary workflow for road surface extraction that could help the state administration reduce costs in the cartography updating task. The main contributions of this work can be summarized as follows:  
1. We contrast the performance of state-of-the-art segmentation architectures and proposed hybrid segmentation models trained for the road extraction task and analyse the effect of the interaction between the architectures and the base networks (backbones). 2. Different from previous works, we focus on challenging scenes where shadows and occlusions are present, and take into account roads present on a variety of soil types, that are easily confused with the surroundings (secondary routes), further complicating the object's extraction. 3. We carry out the experiments on a new dataset composed of tiles with a spatial resolution of 0.50 m and their corresponding annotated reference masks covering representative areas of the Spanish national territory. 4. To visually check the validity of our contributions, we built a system that combines the model's predictions with existing data. The web platform allows the users to assess the quality of the segmentation results and naturally detect the limitations of the study, in order to propose future directions. The remainder of this article is organized as follows. Section 2 presents works related to road extraction using remote sensing data and machine learning techniques.