# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
## 4 Result  
Our semantic segmentation models (Unet, SegNet, and DeepLabV3) utilized TensorFlow and the segmentation-models library to perform image segmentation. The dataset consisted of a total of 765 images, divided into training and testing sets with a ratio of 0.80 and 0.20, respectively. Each image was annotated with one of seven classes: grasslands, arable land, herb-dominated habitats, hedgerows, vineyards, tree-dominated man-made habitats, and _Olea europea_ groves. Initially, the images and their corresponding masks were loaded from the specified directories and pre-processed accordingly. The masks were encoded using label encoding and then split into training and testing datasets. Next, the model was configured with specific parameters, including the EfficientNet backbone architecture and a combination of Dice loss and Categorical Focal loss as optimization objectives. To address the class imbalance inherent in the dataset, we implemented class weighting based on Dice scores. This technique assigns higher weights to classes with lower coverage, ensuring that the model learns effectively from all classes. The model was trained using hyperparameters, as shown in Table 1. After training, the model was saved for future reference. Performance metrics such as accuracy, precision, recall, F1 score, Jaccard score, and mean IoU were computed using the trained model on the test dataset. Finally, the results were printed to evaluate the segmentation model's performance. Table 2 shows a comparative analysis of performance metrics for the three Unet, SegNet, and DeeplabV3 semantic segmentation models under two distinct conditions: with and without backbone integration. These metrics serve as quantitative indicators of model effectiveness in accurately segmenting images. Across all models, the incorporation of backbone architecture consistently leads to improvements in performance metrics. Specifically, models equipped with backbone demonstrate enhanced accuracy, precision, recall, and F1 scores compared to their counterparts lacking backbone integration. Accuracy, which  
measures correct pixel classification, ranges from 0.638 to 0.763 with backbone and from 0.667 to 0.762 without it. Similarly, precision, recall, and F1 scores exhibit higher values when using backbone architecture. Additionally, metrics like the Jaccard coefficient (IOU) and mean IOU, which evaluate the overlap between predicted and ground truth masks, show substantial improvements with backbone integration. These findings underscore the importance of incorporating pre-trained feature extraction networks, or backbones, to enhance the performance of semantic segmentation models, thereby advancing their applicability in diverse image analysis tasks. Based on the performance metrics provided, it can be observed that the DeeplabV3 model consistently outperformed the Unet and SegNet models across the metrics, especially when backbone integration was considered. With backbone integration, DeeplabV3 achieved the highest values for accuracy, precision, recall, F1 score, Jaccard coefficient (IOU), and mean IOU among the three models. This indicates that DeeplabV3, especially when equipped with backbone architecture, offers superior image segmentation capabilities compared to Unet and SegNet. Therefore, in terms of overall performance and effectiveness in segmenting images, DeeplabV3 stands out as the preferred model choice. Our investigation also evaluated the effectiveness of various pre-trained backbones for semantic segmentation of land cover using DeepLabV3. As shown in Table 3, Efficient NetB0 emerged as the leader, achieving the highest overall accuracy (76.35%) and F1-score (75.60%). This indicates its superior ability to accurately segment land cover classes in the imagery. Resnet-34 followed closely with an accuracy of 70.83% and F1-score of 70.77%. While InceptionV3 and DenseNet exhibited lower overall accuracy (around 71-75%), they maintained good precision and recall values for land cover class segmentation (refer to