# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
## 5 Discussion  
In this study, we addressed key challenges and contributed significantly to the field of land cover mapping in agricultural areas. By focusing on semantic segmentation and using satellite imagery and deep learning models with a transfer learning backbone, we provided insights into the effectiveness of advanced techniques in accurately delineating land cover classes. We introduced a land cover dataset annotated for seven distinct classes: grasslands, arable lands, herb-dominated habitats, hedgerows, vineyards, tree-dominated man-made habitats, and _Olea europea_ groves. This dataset addresses a critical gap in the existing literature [32, 33, 34] by enabling comprehensive analysis of multiple land cover types within agricultural landscapes. Using this dataset, we developed a methodology for mapping the distribution of key land cover classes (grasslands, herb-dominated, and tree-dominated areas) across large areas of Francicorta in Lombardy, Northern Italy. This mapping provides a foundational dataset for future research that studies the impact of surrounding land cover on vineyard ecosystem biodiversity and sustainability. However, our approach also addresses a drawback highlighted in previous studies [17, 18, 19]. While traditional land cover mapping techniques have utilized classical machine learning algorithms and object-based image analysis (OBIA), they often struggle with spectral signature similarities, class heterogeneity, and limited generalizability. Additionally, acquiring specialized datasets for semantic segmentation has been challenging due to the high costs associated with data collection and manual annotation. With careful data gathering, preparation, and model selection, we proved that deep learning models like UNet, SegNet, and DeepLabV3 excel in accurately mapping different land cover types from satellite imagery. By integrating pre-trained feature extraction networks, or backbones, into these models, we observed significant improvements in segmentation performance across various metrics. Our findings underscore the importance of leveraging advanced methodologies to achieve more accurate and reliable land cover mapping results. These advanced methodologies offer scalable and efficient solutions for environmental monitoring, conservation, and land management. By harnessing the power of satellite imagery and deep learning models, we can gain valuable insights into ecosystem health, biodiversity conservation, and land use dynamics. The performance metrics of various segmentation models, as presented in Table 2, clearly demonstrate the superiority of the DeepLabv3 model with a backbone in achieving higher segmentation accuracy compared to UNet and SegNet. More specifically, DeepLabv3 with a backbone attained an accuracy of 0.763, a precision of 0.761, a recall of 0.763, an F1 score of 0.756, a Jaccard coefficient (IoU) of 0.626, and a mean IoU of 0.520. These metrics consistently outperform those of the other models tested with and without backbones. The enhanced performance of DeepLabv3 can be attributed to its advanced architecture, which integrates atrous spatial pyramid pooling (ASPP) and robust backbone networks, thereby facilitating multi-scale context aggregation and improved handling of complex spatial patterns [41]. This enables DeepLabv3 to excel in capturing fine-grained details and context, resulting in more accurate and reliable segmentation outcomes. Among the evaluated backbones presented in Table 3, EfficientNetB0 demonstrates the highest performance across most metrics, including accuracy, precision, recall, F1 score, mean IOU, and Jaccard coefficient. This superior performance is due to EfficientNetB0's efficient use of computational resources while maintaining high accuracy and robustness in feature extraction. EfficientNetB0 leverages a compound scaling method that uniformly scales depth, width, and resolution, enabling it to achieve a balanced trade-off between model complexity and performance [36].