## 4 Methodology and Experiments
In broad terms, all these models follow the encoder-decoder pattern presented in Figure 2.  
The fifteen proposed configurations and the original architectures were trained with sets of input tiles and their corresponding rasterized segmentation masks via stochastic gradient descent to optimize the model's weights using estimations of the gradient and to approximately learn the unknown data distribution given the labelled training data. The models take an RGB image of size \(256\times 256\times 3\) and output a segmentation map with the number of channels equal to the number of filters in the last transposed convolution layer. This third dimension is squeezed down using a 1-by-1 convolution layer to the number of classes and subsequently collapsed into a single segmentation map (of size \(256\times 256\times 1\)) by keeping the depth-wise argmax of each pixel. We begin the pre-processing by normalizing the intensity values of the pixels between 0 and 1 (dividing them by 255). As a means of overcoming the limited training dataset, we controlled the overfitting behaviour by applying aggressive data augmentation to the training set described above [50]. These transformations included random crops, horizontal and vertical flips, random rotation and brightness, contrast and gamma shifts, random scale rotation, elastic transformations, and grid and optical distortions. The aggressive data augmentation also helps with the radiometric unbalance present among the Spanish cartography at the national level, by exposing the model to more aspects of the data. As for the hyperparameters, we used Adam [51] (considered to be the fastest to converge [52]) to optimize the binary crossentropy Jaccard loss function. This loss function measures the similarity between the prediction (\(pr\)) and the ground-truth (\(gt\)) when working with a network ending in a sigmoid function, \(L(gt,pr)=-gt\times log(pr)-(1-gt)\times log(1-pr)\), the Jaccard criterion being \(1-(A\cap B/A\cup B)\)  
Figure 2: Example of processing a random tile through the encoder–decoder structure. for any two sets, \(A\) and \(B\). The starting learning rate of 0.01 was reduced with a factor of 0.1 when performance metrics plateaued for more than 10 epochs up to a minimum of \(1\times 10^{-5}\) in all training scenarios. In addition, we set the IoU score as the metric to monitor, the learning process stopping when the performance stalled for more than 10 epochs or when the network started to display overfitting behaviour. For weights initialization of the proposed configurations, we applied transfer learning (called Feature Mapping in segmentation tasks) from the ILSVRC dataset [7] to start with pre-trained weights instead of random initialized ones, this way ensuring a good and faster convergence even when the amount of available training data is insufficient to train a model from scratch [47; 53; 54; 55]. In [55], it was proven that a good convergence can be achieved by fine-tuning the convolutional layers in the encoder path, even if the feature types of the source and the target task differ. For comparison, the original architectures were trained from scratch (using random weight initialization).