A Scale-Aware Machine Learning Framework for Automated GSI Estimation: From Terrestrial to Planetary Environments

Description Electronic Supplementary Materials (ESM) for the research paper titled: "A Scale-Aware Machine Learning Framework for Automated GSI Estimation: From Terrestrial to Planetary Environments", presented at the 77th International Astronautical Congress (IAC 2026). This repository contains the dataset, source codes, and numerical results required to reproduce the findings of the study. Contents: ESM_1_Dataset.zip: Contains 400 rock mass images organized into two domains: Terrestrial_Images: 200 expert-annotated images from Earth (various lithologies), curated from publicly available geotechnical literature. Martian_Images: 200 raw images from NASA's Curiosity and Perseverance rover missions used for direct transfer testing. ESM_2_SourceCode.zip: MATLAB source codes for the proposed framework. Includes scripts for: MobileNetV2 feature extraction. Scale Factor (SF) calculation and integration. Training the Dynamic Ensemble Selection model (GBM, SVR, KNN). Martian GSI prediction (direct transfer without fine-tuning). ESM_3_NumericalData.zip: Contains CSV and Excel files: Ground truth labels (GSI scores) and Scale Factors. Validation results comparing author-assigned vs. literature labels. Model prediction outputs for the Martian dataset.