Comparative Analysis of Fine-Tuned Foundation Models for Land Cover Classification using Sentinel-2 Imagery, Study Area: Sumatra and Kalimantan, Indonesia

Abstract. Land cover classification plays a pivotal role in understanding and managing Earth's resources, influencing decisions in agriculture, forestry, urban planning, and environmental conservation. This study evaluates the performance of fine-tuning the Prithvi Foundation Model, Clay Foundation Model, and U-Net++ with Sentinel-2 imagery for land cover classification in the regions of Kalimantan and Sumatra, Indonesia. Using a dataset of Sentinel-2 image tiles labelled with 12 land cover classes, the models were trained and assessed using Intersection over Union (IoU) metrics. Results demonstrate the superior performance of the Clay Foundation Model, achieving a mean IoU of 0.4819. This research paper highlights the potential of Vision Transformer-based foundation models in distinguishing complex land cover categories and suggests directions for future research.