Digital Elevation Model enhancement using Deep Learning

2021/01/13 by Casey J. Handmer, Handmer, Casey
Earth and Planetary Sciences · Engineering · Physics and Astronomy · #3D Surveying and Cultural Heritage #Computer Vision and Pattern Recognition (cs.CV) #Earth and Planetary Astrophysics (astro-ph.EP) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Planetary Science and Exploration #Robotics and Sensor-Based Localization #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2101.04812

openalex publication_date 2021/01/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Related

• Deep Learning Approaches to DEM Generation: Attention Mechanisms and Gradient-Based Loss Functions
• MCTED: A Machine-Learning-Ready Dataset for Digital Elevation Model Generation From Mars Imagery
• Prompt2DEM: High-Resolution DEMs for Urban and Open Environments from Global Prompts Using a Monocular Foundation Model
• Feedback Neural Network based Super-resolution of DEM for generating high fidelity features
• TTSR: A Transformer-Based Topography Neural Network for Digital Elevation Model Super-Resolution