Supplementary Data for Manuscript "Machine Learning Driven Glacier Thickness Estimation in Diverse Continental Glaciers Using Innovative Pixel Based Skeletonization Approach"

This dataset contains the trained XGBoost-based glacier thickness prediction Machine learning models and the associated GUI tool developed for the manuscript titled: Machine Learning Driven Glacier Thickness Estimation in Diverse Continental Glaciers Using Innovative Pixel Based Skeletonization Approach. The repository includes: Trained XGBoost models for six study glaciers (Bara Shigri, Gangotri, Zemu, Aletsch, Koxkar, and Saskatchewan) The developed GUI-based application for glacier thickness prediction Sample input datasets required to run the model Example output thickness maps本数据集包含为完成题为《基于创新像素骨架化方法的多类型大陆冰川机器学习驱动冰川厚度估算》的学术论文所开发的、经训练的基于极限梯度提升树（XGBoost）的冰川厚度预测机器学习模型，以及配套的图形用户界面（GUI）工具。 该数据集包含以下内容： 1. 针对六个研究冰川（巴拉希格利冰川、甘戈特里冰川、泽木冰川、阿莱奇冰川、科克斯卡尔冰川以及萨斯喀彻温冰川）的经训练XGBoost模型 2. 开发完成的用于冰川厚度预测的GUI应用程序 3. 用于运行模型所需的示例输入数据集 4. 示例输出厚度分布图