Automated Vegetation Segmentation in Mountainous Mediterranean Terrain Using U-Net and UAV Imagery: A Case Study in Dir El Ksiba, Morocco

Abstract

Accurate vegetation mapping in mountainous Mediterranean terrain presents significant challenges due to topographical complexity and accessibility constraints. This study implements a U-Net architecture with MobileNetV2 encoder for automated vegetation segmentation using high-resolution UAV imagery in Dir El Ksiba, Morocco. The methodology leveraged 34 strategically selected images from challenging mountainous terrain, generating 9,996 standardized patches for optimized model training. Our approach targeted five vegetation classes: three phenological stages of Quercus ilex, Pinus halepensis, and Tetraclinis articulata. The U-Net+MobileNetV2 model demonstrated robust performance with validation metrics outperforming training metrics: 82.0% accuracy and mean Intersection over Union (mIoU) of 0.585. Class-specific IoU values revealed highest performance for Pinus halepensis (0.634), followed by Quercus ilex Stage 1 (0.598), Tetraclinis articulata (0.588), Quercus ilex Stage 2 (0.571), and Quercus ilex Stage 3 (0.556). The lightweight architecture (3.5M parameters) enables efficient deployment while maintaining competitive performance. This research establishes a methodological framework for applying deep learning-based vegetation segmentation in topographically complex Mediterranean ecosystems, addressing practical constraints of mountainous terrain data acquisition while maintaining scientific rigor.