Digital Elevation Model enhancement using Deep Learning

• URL: http://arxiv.org/abs/2101.04812v1
• Date: Wed, 13 Jan 2021 00:07:57 GMT
• Title: Digital Elevation Model enhancement using Deep Learning
• Abstract summary: We demonstrate high fidelity enhancement of planetary digital elevation models (DEMs) using optical images and deep learning with convolutional neural networks. Deep learning-based photoclinometry robustly recovers features obscured by non-ideal lighting conditions. Analysis shows enhanced DEM slope errors are comparable with high resolution maps using conventional, labor intensive methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We demonstrate high fidelity enhancement of planetary digital elevation models (DEMs) using optical images and deep learning with convolutional neural networks. Enhancement can be applied recursively to the limit of available optical data, representing a 90x resolution improvement in global Mars DEMs. Deep learning-based photoclinometry robustly recovers features obscured by non-ideal lighting conditions. Method can be automated at global scale. Analysis shows enhanced DEM slope errors are comparable with high resolution maps using conventional, labor intensive methods.

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