## 2 Related Work  
The emergence of modern semantic segmentation techniques was fuelled by the success of Convolutional Neural Network (CNN) architectures. Segmentation models like the Fully Convolutional Network (FCN) [5] are trained using a pixel-wise loss and introduce upconvolutions to resize the image to the input dimensions by making use of the feature maps produced by the CNNs (which act as backbone networks). FCN-8 variant reached a 0.62 mean Intersection over Union (IoU) score (or Jaccard Index) on the 2012 PASCAL VOC (Visual Object Classes) Segmentation Challenge [6] using pre-trained models on the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [7]. The IoU score measures the similarity between sample sets by dividing the number of pixels in the intersection between the ground-truth mask and the predicted segmentation map to the size of the union of the two. The IoU score can be calculated by means of the True Positives (_TP_), False Positives (_FP_) and False Negatives (_FN_) obtained from the confusion matrix of the segmentation classifier, _IoU = TP/TP + FP + FN_, and is widely used in image segmentation tasks. A value closer to 1 is desired; a score higher than 0.5 is considered a good prediction [8]. State-of-the-art segmentation models are focusing on a better understanding of the local context by linking different parts of the input image to improve the predictions and learn the relations between the objects. Recent architectures are generally composed of two linked parts: a convolutional part (or encoder), which downsamples the input image using convolutional and Rectified Linear Units (ReLU) layers [9] to generate a vector of features, and an upconvolutional part (or decoder), which uses transpose convolutions to expand the feature maps to match the input size while keeping the information location in the resulting maps. The upsampling part takes the vector of features as input and generates a map with the probabilities of belonging to a class for each pixel. One example of such architecture is U-Net [10], which received widespread interest in the scientific community and was continuously extended in recent works (LinkNet, Feature Pyramid Networks (FPN), Pyramid Scene Parsing Networks (PSPNet), etc.). LinkNet [11] uses the same U-shape structure, but replaces the convolutions in each of the encoder and decoder steps with residual blocks [12], while FPN [13] feed the outputs of each downsampling step into a pyramid level and use lateral connections to join low-resolution and high-resolution features, allowing the detection of objects at multiple scales. The backbone networks also evolved to improve the efficiency of semantic segmentation. Moving away from using standard CNN architectures like VGGNet [14] or Inception-V3 [15] as backbone networks, architectures specialized in image segmentation emerged. One such example is MobileNetV2 [16], which obtains high computational efficiency by inserting linear bottleneck layers encoded by bottleneck inputs into the convolutional blocks. EfficientNet [17] focused on proposing a model scaling method and shows that balancing a network's depth, width, and resolution can lead to great improvements in performance. SEResNeXt uses \"Squeeze-and-Excitation\" (SE) blocks [18] that model the interdependencies between channels at the end of each non-identity branch of ResNeXt's [19] residual blocks (a ResNet-based architecture that introduced a hyper-parameter called number of independent paths, or cardinality, to adjust the model's capacity). These computer vision advances enabled the automatic extraction of information from satellite imagery. The majority of works involving remotely sensed imagery tackle land cover classification tasks, where different land use classes are extracted from aerial images to produce maps (e.g., plantations, buildings, forests, etc.).