## 4 Experiments
### Implementation and Results
#### 4.2.1 Loss Function Choice
The model trained with Cross entropy shows bit higher results over Corsican data, which is the set of data with the higher quota of Fire pixels in terms of mean accuracy and mean IoU. So, the overall results tend to elite this model as the best one. Nevertheless, the model trained with Dice loss draws the best results over the most challenging aerial datasets. This model, named _Model_4_, achieves a global accuracy of 98.65%, a mean accuracy of 88.92%, a mean, and a weighted IoU of 85.78% and 97.40%, respectively, a mean BF score of 93.75%. Figure 8: The overall metrics were gathered using different loss functions with Deeplabv3+ with ResNet-18 as a backbone. Figure 7: The overall metrics, over the three sets of data (FLAME, Corsican, and Firefront_Gestosa), gathered using Tversky and Dice loss functions with Deeplabv3+ with InceptionResNetV2 as backbone. Nonetheless, the results for deeplabv3+ trained with RestNet-50 within the four different loss functions previously introduced are depicted in Figure 10. Both models trained within the Dice and Tversky loss functions show similar results in mean IoU even though the one trained with Dice overpassed the one trained with Tversky in terms of mean accuracy and BF score. So, it was necessary to analyze the results over every set of data individually. Figure 10: The overall metrics gathered using different loss function with Deeplabv3+ with ResNet-50 as backbone. Figure 5:0 The overall metrics, over the three sets of data (FLAME, Corsican, and Firefront_Gestosa), were gathered using Cross entropy and Dice loss functions with Deeplabv3+ with ResNet-18 as a backbone. The chart of Figure 11 present the results of the models trained with Tversky and Cross entropy loss functions over every data set. The model trained with Tversky loss gives a higher value of mean IoU and mean BF score over the Corsican set. Although the model trained with Dice loss attains the best results in terms of mean accuracy, mean IoU, and mean BF score over the two aerial sets of data. This model, named _Model_5_, reaches a global accuracy of 98.65%, a mean accuracy of 90.86%, a mean and a weighted IoU of 86.27% and 97.44%, respectively, and a mean BF score of 93.63%. To resume, cross-entropy loss in her original formulation could not handle imbalanced data problems and complex data cases. The dice loss concentrates on the positive sample regions and is less sensitive to the imbalanced data problem. Tversky loss relies on the principle of weighing FN and FP by tuning the value of \(b\) bigger than \(a\) to penalize FN. However, this function remains a tentative improvement of the Dice function to have more control over loss in case of unbalanced datasets to promote the results of the segmentation of small-scale objects. Nonetheless, focal loss introduces a mathematical constant (\(1-\gamma\)) that causes less sensitivity to the imbalanced data problem. Furthermore, this function is better suited to locate small and fine boundary objects that are difficult to identify accurately, and it can handle complicated misclassified data samples. Those two improvements of Dice loss allow detecting small-scale objects with finer boundary, which is evident through the results, i.e., a slight improvement of the metrics over Corsican data in most cases. This set has the bigger contribution of pixel fires in the final dataset. However, as previously stated, the proposed framework is more suited for aerial data, making it more challenging owing to the lack of publicly available data for training.