High-fidelity lunar topographic reconstruction across diverse terrain and illumination environments using deep learning

2026-01-14

AI Summary

This study addresses the scarcity of meter-scale lunar topographic data, particularly in complex terrains and low-illumination regions such as the permanently shadowed areas near the lunar south pole, which severely limits high-resolution geological analysis. To overcome this challenge, the authors propose a deep learning–based method for reconstructing high-fidelity terrain from single-view images by integrating shape-from-shading cues with low-resolution topographic constraints and introducing a robust scale recovery mechanism. This approach achieves, for the first time, accurate and detailed terrain reconstruction in challenging environments including polar and permanently shadowed regions, maintaining consistent performance across diverse surface morphologies and lighting conditions. The work significantly expands the applicability of deep learning techniques in lunar remote sensing and cartography.

Technology Category

• Machine Learning: Learning with Manifolds
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Topographic models are essential for characterizing planetary surfaces and for inferring underlying geological processes. Nevertheless, meter-scale topographic data remain limited, which constrains detailed planetary investigations, even for the Moon, where extensive high-resolution orbital images are available. Recent advances in deep learning (DL) exploit single-view imagery, constrained by low-resolution topography, for fast and flexible reconstruction of fine-scale topography. However, their robustness and general applicability across diverse lunar landforms and illumination conditions remain insufficiently explored. In this study, we build upon our previously proposed DL framework by incorporating a more robust scale recovery scheme and extending the model to polar regions under low solar illumination conditions. We demonstrate that, compared with single-view shape-from-shading methods, the proposed DL approach exhibits greater robustness to varying illumination and achieves more consistent and accurate topographic reconstructions. Furthermore, it reliably reconstructs topography across lunar features of diverse scales, morphologies, and geological ages. High-quality topographic models are also produced for the lunar south polar areas, including permanently shadowed regions, demonstrating the method's capability in reconstructing complex and low-illumination terrain. These findings suggest that DL-based approaches have the potential to leverage extensive lunar datasets to support advanced exploration missions and enable investigations of the Moon at unprecedented topographic resolution.

Problem

• lunar topography
• high-fidelity reconstruction
• illumination variability
• diverse terrain
• meter-scale data

Innovation

• deep learning
• topographic reconstruction
• lunar surface
• shape-from-shading
• permanently shadowed regions