Methods for evaluating the resolution of 3D data derived from satellite images

2025/06/13 by Christina Selby, Selby, Christina, Holden Bindl +11
Earth and Planetary Sciences · Engineering · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Geological Modeling and Analysis #Image and Video Processing (eess.IV) #Remote Sensing and LiDAR Applications #Satellite Image Processing and Photogrammetry #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2506.11876

openalex publication_date 2025/06/13 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28

Abstract

3D data derived from satellite images is essential for scene modeling applications requiring large-scale coverage or involving locations not accessible by airborne lidar or cameras. Measuring the resolution of this data is important for determining mission utility and tracking improvements. In this work, we consider methods to evaluate the resolution of point clouds, digital surface models, and 3D mesh models. We describe 3D metric evaluation tools and workflows that enable automated evaluation based on high-resolution reference airborne lidar, and we present results of analyses with data of varying quality.

Citations

• FusionRF: High-Fidelity Satellite Neural Radiance Fields from Multispectral and Panchromatic Acquisitions
• BRDF-NeRF: Neural Radiance Fields with Optical Satellite Images and BRDF Modelling
• Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds
• STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
• Sat-NeRF: Learning Multi-View Satellite Photogrammetry With Transient\n Objects and Shadow Modeling Using RPC Cameras
• Cumulative Assessment for Urban 3D Modeling
• SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization
• DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation
• Leveraging Vision Reconstruction Pipelines for Satellite Imagery
• ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE

Related

• Automated 3D recovery from very high resolution multi-view satellite images
• 3D Lidar Mapping Relative Accuracy Automatic Evaluation Algorithm
• Unconstrained Large-scale 3D Reconstruction and Rendering across Altitudes
• DSMNet: Deep High-precision 3D Surface Modeling from Sparse Point Cloud Frames
• Metrics for Aerial, Urban LiDAR Point Clouds