# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
## 6 Conclusions  
This study demonstrates the feasibility of employing satellite imagery and deep learning to accurately map land cover in agricultural regions. By developing a cost-effective and reproducible method, we created a novel dataset specifically tailored to wine-growing areas within the Franciacorta area. This dataset comprises seven manually annotated land cover classes, providing a valuable resource for agricultural research. Through rigorous comparison of state-of-the-art deep learning models, our research contributes significantly to advancing land cover mapping techniques.  
The results demonstrate the effectiveness of deep learning models, particularly DeepLabv3, in accurately segmenting land cover classes from satellite imagery. DeepLabv3 with an EfficientNetB0 backbone outperformed other models in all performance metrics, making it the most reliable for land cover mapping. Its advanced architecture integrates atrous spatial pyramid pooling (ASPP) and efficient multi-scale context aggregation, enabling it to capture fine-grained details and handle complex spatial patterns effectively. This study highlights the potential of these advanced methods for supporting sustainable land management, environmental monitoring, and agricultural decision making. Furthermore, by analyzing different land cover types such as grasslands, herb-dominated habitats, and tree-dominated areas, we emphasize the importance of understanding how these surrounding habitats contribute to vineyard sustainability, fostering biodiversity, and enhancing ecosystem services like pollination and pest control.  
Compared to traditional manual field-based mapping approaches, our methodology reduces the time required for data preparation by automating annotation tasks and utilizing open-access satellite imagery. Performance metrics show that DeepLabv3 with EfficientNetB0 improves segmentation accuracy by 10.9-13.3% and F1 score by 11.0-12.4% over other tested models. Additionally, it significantly lowers costs by avoiding the need for expensive high-resolution proprietary imagery and specialized equipment. While this research offers valuable insights, further investigations are necessary to enhance the generalizability of the findings to diverse agricultural regions. Expanding the dataset to include additional land cover classes and incorporating ground-truth data through field surveys are essential for refining model performance and expanding the application of these techniques. Moreover, this approach investigates the application of more advanced deep learning architectures.  
Conceptualization, G.T., G.G., I.S. and I.G.; methodology, G.T.; software, G.T., A.S.M., L.F. and C.B.; validation, I.G., G.G., I.S. and A.S.; formal analysis, G.T., I.G., G.G. and I.S.; investigation, G.T.; resources, G.T., A.S.M. and C.B.; data curation, G.T. and C.B.; writing--original draft preparation, G.T.; writing--review and editing, G.T., I.G., G.G. and I.S.; visualization, I.G. and G.G.; supervision, G.G.; project administration, G.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been partially supported by \"Fondazione Cariplo\" (Italy) and \"the Lombardy Regional Government Authority\" (Italy) under the project entitled: Progetto \"Biodiversita, suolo e servizi ecosistemici: Metodi e tecniche per food system robusti, resilienti e sostenibili\"--Bando Emblematici Maggiori 2020 and Climate Change AI project (No. IG-2023-174).  
**Data Availability Statement:** The dataset presented in this study is available at the link [[https://doi.org/10.5281/zenodo.12189081](https://doi.org/10.5281/zenodo.12189081)]([https://doi.org/10.5281/zenodo.12189081](https://doi.org/10.5281/zenodo.12189081)) (accessed on 20 June 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest.