Advancing Earth science applications through a semi-automatic monoplotting framework for efficient 3D geo-referencing of monocular oblique visual Data

Abstract

The widespread availability of high-quality images from smartphones, drones, and digital cameras presents an unprecedented opportunity for global geospatial data collection. However, these images are often captured at oblique angles, making geo-referencing challenging and limiting their usability. Monoplotting, a technique that requires only a single image and a Digital Elevation Model (DEM), addresses these challenges by establishing pixel-level correspondence between imagery and real-world coordinates. However, traditional monoplotting methods are labor-intensive, requiring manual identification of control points in both the image and DEM, as well as manual tuning of camera parameters, which restricts scalability for large-scale databases and near-real-time applications. This paper proposes a novel semi-automatic monoplotting framework that minimizes human intervention. The framework integrates key point detection, geo-referenced 3D point retrieval, regularized gradient-based optimization, pose estimation, back-projection, and pixel mapping to enable efficient geo-referencing. To the best of the authors’ knowledge, this is the first study to incorporate key point detection into monoplotting, and apply regularized gradient-based optimization for camera position and parameter determination, even with unequal numbers of key points from the image and DEM. Numerical experiments with a historical image and a corresponding real-world DEM demonstrate the framework’s effectiveness. The robustness of the method is further evaluated on distorted images, where the distortion strength coefficient is treated as an unknown and estimated through projection optimization. The results confirm the framework’s ability to establish accurate correspondence between the image pixel domain and real-world 3D coordinates. Additionally, integrating machine learning models, such as semantic segmentation, highlights the framework’s advantages in Earth science applications, including snow and glacier characterization.