Deep learning model generates lunar elevation maps from single satellite images

Researchers have developed LunarDepthNet, a novel deep learning model designed to generate detailed Digital Elevation Models (DEMs) of the lunar surface using monocular satellite images. The model employs a UNet architecture with an EfficientNet encoder and custom layers to accurately interpret shadow information and estimate elevation. LunarDepthNet achieved a mean nRMSE of 0.437 and an MAE of 4.5m, demonstrating its capability to produce reliable elevation maps, particularly for lunar regions lacking stereo imagery. AI

IMPACT Enables creation of lunar elevation maps from single images, aiding lunar exploration and mission planning in data-scarce regions.

RANK_REASON This is a research paper detailing a new deep learning model for generating lunar elevation maps.

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• arXiv
• Chandrayaan-2
• EfficientNet
• LunarDepthNet

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