A Machine Learning Approach to Detecting Albedo Anomalies on the Lunar Surface

TL;DR

This paper presents a machine learning framework that predicts lunar surface albedo anomalies by analyzing elemental composition data, enhancing understanding of planetary surfaces and enabling predictions in data-sparse regions.

Contribution

The study introduces an innovative adaptive Gaussian blurring technique and applies an Extreme Gradient Boosting Regression Model for lunar albedo prediction based on chemical elements.

Findings

Successful prediction of lunar albedo using ML models

Identification of elemental relationships with surface reflectivity

Development of an interactive tool for error visualization

Abstract

This study introduces a data-driven approach using machine learning (ML) techniques to explore and predict albedo anomalies on the Moon's surface. The research leverages diverse planetary datasets, including high-spatial-resolution albedo maps and element maps (LPFe, LPK, LPTh, LPTi) derived from laser and gamma-ray measurements. The primary objective is to identify relationships between chemical elements and albedo, thereby expanding our understanding of planetary surfaces and offering predictive capabilities for areas with incomplete datasets. To bridge the gap in resolution between the albedo and element maps, we employ Gaussian blurring techniques, including an innovative adaptive Gaussian blur. Our methodology culminates in the deployment of an Extreme Gradient Boosting Regression Model, optimized to predict full albedo based on elemental composition. Furthermore, we present an interactive analytical tool to visualize prediction errors, delineating their spatial and chemical characteristics. The findings not only pave the way for a more comprehensive understanding of the Moon's surface but also provide a framework for similar studies on other celestial bodies.