# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
## 2 Materials and Methods
### Data Acquisition  
The total study area covers 2981.4 hectares. We divided this area into 18 image tiles. Among these tiles, the smallest covers 14 hectares with a 1192 \(\times\) 700-pixel picture, and the largest 172 hectares with a 2392 \(\times\) 1402-pixel picture. The following steps were taken to prepare the picture and the corresponding mask image:  
Step 1: The process initiated with the acquisition of satellite imagery from Google Earth Pro by capturing both the visual image and its accompanying shape file in.kmz format as shown in Figure 3. This imagery encompasses a broad spatial scope and provides a comprehensive view of the target area. Subsequently, the acquired images were georeferenced using ArcGIS 10.3 software, a crucial step in aligning the satellite image with geographic coordinates for accurate spatial analysis. Figure 1: General workflow of semantic segmentation. Step 2: After georeferencing, multiresolution segmentation (MRS) in eCognition partitioned the imagery into homogeneous regions based on spectral and spatial characteristics. The scale parameter was set to 100, compactness to 0.5, and shape to 0.4; these values optimized the balance between spatial coherence and object delineation. A georeferenced shapefile was then generated. This facilitated expert masking in QGIS to remove unnecessary fragmented objects. Step 3: Following segmentation, the resulting segmented shape file and image were subject to meticulous examination and validation by a knowledgeable plant expert as shown in Figure 3.  
Figure 3: Satellite imagery acquisition and georeferencing process. Figure 2: Geographic coverage of the study area in Franciacorta, Lombardy, Northern Italy. Here Italy is outlined in red, Lombardy in yellow, and the study area in blue. in Figure 4. Leveraging the capabilities of QGIS software 3.24.0, the expert meticulously inspected each segmented polygon and assigned appropriate plant names based on their botanical characteristics and distribution within the landscape. Classes:  
1. Grassland: grasslands are crucial for maintaining biodiversity in vineyard ecosystems in that they support various pollinators, herbivores, and predators [3]. These areas provide habitat for beneficial insects, such as bees and butterflies, which aid in pollination, a key service for vineyards. Additionally, grasslands can foster natural pest control by harboring insectivorous species that help keep pest populations in check [2]. 2. Arboreal land: tree-dominated habitats, such as forests or woodlands, are vital for fostering biodiversity as they offer shelter and food resources for a variety of species, including birds, mammals, and insects. These areas also play a critical role in climate regulation and contribute to ecosystem connectivity. In vineyard landscapes, arboreal land acts as a buffer zone that protects against wind erosion, provides shade, and enhances the resilience of the ecosystem as a whole. 3. Herbaceous habitats: herbs and grasses are important to foster biodiversity in that they support herbivores, pollinators, and decomposers. These areas typically host plant species that help improve soil health and nutrient cycling, and hence the sustainability of surrounding agricultural areas. In vineyards, herb-dominated habitats can play a role in reducing soil erosion, supporting beneficial micro fauna, and providing a diverse range of species that contribute to the overall ecosystem. 4. Hedgerows: these are critical for maintaining ecosystem connectivity in agricultural landscapes. These linear strips of vegetation provide a corridor for wildlife movement, as they offer shelter, food, and protection for pollinators, birds, and small mammals. In vineyards, hedgerows can also act as natural pest control systems by supporting predatory species that reduce the abundance of vineyard pests, such as aphids or grapevine moths. 5. Vineyards: as the primary agricultural feature in this study, vineyards play a dual role in biodiversity.