# ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery
## 5 Results  
We compared our solution against some state-of-the-art CNNs and variants to measure the effectiveness of the proposed approach. In particular, we compared against some variants of three main architectures:  
* We trained an FCN-8s, a variant of an FCN model introduced in Section 2. The classical FCN architecture consists of a series of convolutional and pooling layers, with FCN-8s also implementing a fusing strategy between predictions of the shallower layer Pool3 with twice-up-sampled sum of the two predictions derived from Pool4 and the last layer. The stride 8 predictions are subsequently up-sampled back to the image;
* We investigated the use of a standard U-Net using different pre-trained CNNs as encoders. In particular, we explored the use of VGG16 [41], ResNet [8] and EfficientNet [42] architectures pre-trained on ImageNet. The reasons behind this choice are the high generalization ability demonstrated over the years by VGG, the ability to deal with gradient vanishing of ResNet and the high efficiency/performance trade-off of EfficientNet;  
Figure 10: Learning Rate (LR) tuning analysis for the ReFuse model. (**a**) The output of the LR range test. The figure highlights the maximum learning rate before loss increases. (**b**) Learning rate values during training with the one-cycle policy [39]. The one-cycle strategy increases the learning rate from an initial value to a maximum learning rate. It then decreases the learning rate from the maximum value to some minimum, which is substantially lower than the starting learning rate. * We used also DeepLabv3+ [43], an architecture introducing changes to the encoder-decoder structure, such as the use of dilated convolutions [44], to preserve most of the spatial input information. We tested these architectures with different bands as input (i.e., RGB, RGB + NIR, all 13 bands resized to have the same spatial resolution). In all the experiments, as evaluation metrics, we used the pixel-wise segmentation accuracy and the Intersection over Union (IoU), defined as:  
\[IoU=|Predicted\cap GT|/|Predicted\cup GT|\]  
where \(GT\) is the ground-truth imperviousness mask. Table 2 reports the results for the considered analysis, reporting for each configuration the base architecture, the used encoder (if any), the used input bands and the obtained performance. The table clearly shows that the proposed approach outperforms all the considered competitors by a large margin. Moreover, analysing the table, there are a few points worth highlighting:  
* Models trained on RGB bands and using transfer learning (with weights pre-trained on ImageNet) tend to perform better than those using a different combination for the bands;
* Results obtained using all bands at 10 m spatial resolution, i.e., RGB and NIR, are slightly comparable with results obtained with RGB bands only;
* ResNet-50 tends to be the most effective encoder;
* Using all 13 bands causes the worst results both in terms of accuracy and IoU. This confirms our claim that using more bands does not necessarily result in better performance. To better understand the effectiveness of the proposed approach, in Figure 11 we report an inference example on a single patch, highlighting false positives (FP) and false negatives (FN). Interestingly, ReFuse produces a segmentation mask very close to the ground truth, with errors almost all located along the borders of the urban areas or in shadow zones (misclassified as impervious areas). Similarly, Figure 12 reports the extraction results from the test set of two representative regions in the city of Turin in Italy, including small dense residential buildings and large high buildings in urban commercial areas. For both areas, the model produces excellent segmentation masks. Both images in Figure 12d,h reveal some difficulty in segmenting small buildings and noise along the edges of streets. This result can be explained in the first instance by the spatial resolution of the Sentinel-2 data. A