Accurate soil type categorization is very important for resource management in space exploration. Using a complete system including a space station, rovers, and a deep learning framework, this study proposes an advanced deep learning model for soil type categorization in space informatics. Gathering and preprocessing multispectral and hyperspectral soil data, the rovers send it to the space station for in-depth study. Our model had a test accuracy of about 80%. For space informatics, the suggested method guarantees strong and accurate soil categorization, therefore enabling efficient decision-making.

中文翻译：

---

在空间信息学中推进土壤类型分类的深度学习


准确的土壤类型分类对于太空探测中的资源管理非常重要。利用包括空间站、漫游车和深度学习框架在内的完整系统，本研究提出了一种先进的深度学习模型，用于空间信息学中的土壤类型分类。漫游车收集和预处理多光谱和高光谱土壤数据，将其发送到空间站进行深入研究。我们的模型的测试准确率约为 80%。对于空间信息学，所建议的方法保证了强大而准确的土壤分类，从而能够实现高效的决策。