Semantic Segmentation Using Deep Learning: Insights from the LICAID Dataset

Land cover mapping is critical for monitoring global land use patterns, assessing ecosystem health, and supporting conservation efforts. However, challenges persist in handling large satellite imagery datasets and acquiring specialized aerial datasets for deep-learning models. To address these challenges, this study introduces a methodology for semantic segmentation of land cover in agricultural regions, specifically tailored to the wine-growing region of Franciacorta, Italy. We present the "Land Cover Aerial Imagery" (LICAID) dataset and employ the advanced deep learning model DeepLabV3 with various pre-trained backbones (ResNet, DenseNet, and EfficientNet) for comparative analysis. The dataset comprises eleven land cover classes: grasslands, arable land, herb-dominated habitats, hedgerows, vineyards, tree-dominated man-made habitats, olive groves, wetlands, lines of planted trees, small anthropogenic forests, and others. Results demonstrate significant performance improvements in land cover classification using deep learning with pre-trained networks, providing a scalable and cost-effective approach to land cover mapping that supports environmental monitoring and conservation.