Application of Fine-Tuned Segment Anything Model with Google Maps to Forest Road Extraction from CAS500-1 Satellite Images: A Case Study

This study investigates the feasibility of a fine-tuned Segment Anything Model (SAM) for forest road segmentation, utilizing high-resolution imagery from the Compact Advanced Satellite 500-1 (CAS500-1) as a case study.Due to the lack of labeled or domain-specific datasets for forest roads, a custom training dataset was constructed from Google Maps (GM) imagery, enabling the fine-tuning of SAM with a Vision Transformer-Huge (ViT-H) backbone.The results were evaluated against manually labeled ground truth, OpenStreetMap (OSM), and the Forest Big Data Exchange Platform (FBDEP) in Korea.Evaluation metrics included mean Intersection over Union (mIoU) and area-weighted mIoU (AWmIoU).The finetuned model achieved notable improvements compared to the baseline SAM (mIoU: 0.6190 to 0.7786; AWmIoU: 0.6970 to 0.8369).These findings confirm the value of domain-specific fine-tuning and demonstrate the potential of SAM for extracting forest roads.