## 3 Dataset  
One of the major challenges specific to semantic segmentation is that it requires images annotated at the pixel level; we need aerial orthoimages containing secondary roads and their corresponding segmentation maps. Similar to other existing works [22; 38], we used existent cartographic support to avoid a considerable tagging effort. The data were issued by public agencies and georeferenced in European Terrestrial Reference System 1989 (ETRS89) (compatible with World Geodetic System 1984, WGS84) and Universal Transversal Mercator (UTM) projection in the correspondent zone.  
* Firstly, the needed imagery was obtained from PNOA [39] (National Plan of Aerial Orthophotography, Spanish: \"Plan Nacional de Ortofotografia Aerea\") and has a spatial resolution of 0.50 m and a planimetric RMSE (root-mean-square error) \(\leq 1\) m. The photographs were acquired in optimal meteorological conditions at a low flight altitude using calibrated photogrammetric cameras equipped with 3-band RGB sensors (8 bits per band). The imagery was orthorectified, radiometrically corrected and has topographic corrections applied using ground points measured with accurate GPS systems.
* Secondly, the segmentation masks containing the correspondent geographic information were obtained from the National Topographic Map [40] (Spanish: \"Map Topografico Nacional\") in vectorial format at a scale of 1:25,000. These road vectors were rasterized to create segmentation masks in image format, the resulting masks representing our training labels. One thing worth noting is that the rasterized vectors do not always align completely with the imagery due to the distinct criteria used for tagging (variations in width and contour, depending on the road's importance), due to the error-prone conversion of a vector to a raster or due to human errors. However, as stated in the introduction, we wanted to focus on very challenging instances, with irregular geometries, different spectral signatures of the materials used for pavement, and where obstructions from objects are present. The intuition is that by training the models with real data, they will be better prepared to handle difficult scenarios and will offer an intuition about their expected behaviour in large-scale applications.  
To obtain the training set, an operator performed a visual comparison between the most recent orthoimages available and the existing cartographic support in vector format in different representative areas of the Spanish territory (Andalucia, Castilla y Leon, Castilla-La Mancha, Galicia, Islas Baleares, Murcia and Navarra) and manually tagged the tiles using a WMS (Web Map Service) viewer built for this purpose (described in [41]). We collected our training data as sets of aerial orthoimagery clipped into fixed tiles and their correspondent segmentation mask measuring 256 by 256 pixels (0.07 Megapixels) stored in the lossless png format (examples can be seen in Figure 1).  
The dataset contains 7750 pairs of tiles and their rasterized masks of the ground-truth at pixel level, covers a land area of approximately 175 km\({}^{2}\), and has a size on disk of approximately 1.21 gigabytes. We are working towards increasing its size using crowdsourcing techniques and plan to make it openly available in the near future.