🤖 AI Summary
Satellite-derived 3D geospatial products—including point clouds, digital surface models (DSMs), and 3D meshes—lack a standardized, quantitative resolution metric.
Method: This study proposes the first automated resolution assessment framework tailored to heterogeneous satellite-based 3D products. It integrates 3D metric analysis, robust point cloud registration with error mapping, surface geometric fidelity modeling, and a multi-scale resolution characterization workflow, enabling fully automatic comparison against high-accuracy airborne LiDAR reference data.
Results: Validated across diverse satellite 3D datasets of varying sources and quality levels, the framework delivers quantitative resolution metrics, enables cross-source comparability, and supports accuracy闭环 validation for large-scale 3D scene modeling. It significantly enhances objectivity, reproducibility, and performance traceability of 3D geospatial information products.
📝 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.