Advance deep learning for soil type classification in space informatics

Abstract

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.