AUTOMATIC SEMANTIC SEGMENTATION OF SENTINEL-2 IMAGES: INTEGRATION OF CLUSTERING AND LARGE MULTIMODAL MODELS FOR CLUSTER INTERPRETATION

Semantic segmentation of satellite imagery, particularly Sentinel-2 data, is crucial for environmental monitoring and land cover mapping. This paper presents an unsupervised method for land cover classification that eliminates the need for pixel-level annotations. The approach combines clustering techniques (K-Means, DBSCAN, autoencoders) with automated cluster labeling using large vision-language models (e.g., GPT-4, Claude, Gemini 2.0). Clusters are visualized and interpreted by these models based on spatial context and color. The methodology achieves segmentation accuracy of 85–90%, comparable to supervised methods, while ensuring interpretability and scalability. A majority voting mechanism and terminology normalization improve consistency across model outputs. Validation is performed using ESA WorldCover maps. The proposed approach is promising for rapid land cover mapping in resource-constrained or emergency situations.