Enhancing Satellite Imagery Segmentation with SAM Fine-Tuning

Created using ChatSlide

This presentation explores the SAM model's capabilities for land cover segmentation, highlighting its significance and project objectives. Methodology includes dataset preprocessing, adapting SAM with a classification head, and applying supervised learning with evaluation metrics. Current progress covers achievements in data preparation, training strategies, and performance comparisons with CNN models, while discussing future directions for improved outcomes.