# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
## 4 Result
\begin{table}
\begin{tabular}{c c} \hline
**Hyperparameter** & **Value** \\ \hline Number of epochs & 100 \\ Batch size & 16 \\ Patch size & 128 \\ Dice loss weights & [0.1428, 0.1428, 0.1428, 0.1428, 0.1428, 0.1428] \\ Focal loss & 1.0 (weight in total loss calculation) \\ Loss function & Total loss (Dice loss + focal loss) \\ Optimizer & Adam \\ Input shape & (128, 128, 3) \\ Number of classes & 7 \\ \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters and configurations used in the semantic segmentation model training with pre-trained backbone for land cover segmentation. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Performance** & \multicolumn{2}{c}{**UNet**} & \multicolumn{2}{c}{**SegNet**} & \multicolumn{2}{c}{**DeepLabV3**} \\
**Metrics** & **Without** & **with** & **Without** & **With** & **Without** & **with** \\
**Backbone** & **Backbone** & **Backbone** & **Backbone** & **Backbone** & **Backbone** & **Backbone** \\ \hline Accuracy & 0.574 & 0.653 & 0.564 & 0.673 & 0.681 & 0.763 \\ Precision & 0.590 & 0.657 & 0.563 & 0.678 & 0.6855 & 0.761 \\ Recall & 0.574 & 0.653 & 0.566 & 0.673 & 0.681 & 0.763 \\ F1 score & 0.573 & 0.646 & 0.559 & 0.672 & 0.674 & 0.756 \\ Jaccard & & & & & & \\ coefficient & 0.411 & 0.500 & 0.399 & 0.528 & 0.5308 & 0.626 \\ (IOU) & & & & & & \\ Mean IOU & 0.306 & 0.323 & 0.290 & 0.407 & 0.410 & 0.520 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance metrics comparison of UNet, SegNet, and DeeplabV3 models with and without a backbone. Table 2 for details). Mean intersection-over-union (mIOU) and Jaccard score mirrored these trends, with EfficientNetB0 achieving the highest values (52.05% and 62.60%, respectively). These findings highlight the importance of selecting an appropriate pre-trained backbone for DeepLabV3 in land cover segmentation tasks. The optimal choice can significantly impact the model's ability to differentiate and delineate land cover classes. We analyzed the receiver operating characteristic (ROC) curves, as shown in Figure 9, to evaluate our multi-class classification model's performance. The curves visualize the trade-off between true positive rate (TPR) and false positive rate (FPR) for each class, while the micro-average ROC curve summarizes the overall performance. The area under curve (AUC) quantifies the model's discriminative power. High AUC values, with the micro-average reaching 0.95 and individual class values ranging from 0.93 to 0.94, indicate that our model effectively differentiates between the various land cover classes. Figure 9: Receiver operating characteristic (ROC) curves show the performance of the multi-class land cover classification model. The micro-average ROC curve and individual class ROC curves demonstrate the model’s strong ability to discriminate between different land cover classes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Backbone** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Mean IOU** & **Jaccard Coefficient (IOU)** \\ \hline Resnet 34 & 70.83 & 73.29 & 70.84 & 70.77 & 46.32 & 56.90 \\ EfficientNetB0 & 76.33 & 76.10 & 76.30 & 75.60 & 52.00 & 62.60 \\ InceptionV3 & 71.46 & 74.00 & 71.46 & 72.01 & 47.50 & 61.40 \\ Densest & 75.07 & 76.18 & 75.05 & 74.66 & 50.70 & 61.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of semantic segmentation performance using DeepLabV3 with different pre-trained backbones. After loading the trained model, we generated a batch of test images and their corresponding masks using the validation data generator. Next, we used the loaded model to compute predictions for the test images. Then we converted the predicted masks from categorical format to integer format for visualization and IoU calculation. Finally, in order to qualitatively assess the model's performance, we visualized a randomly selected test image along with its ground truth mask and predicted mask, as shown in Figure 10.  
Figure 10: Display of a randomly selected test image, ground truth mask, predicted mask, and error map.