The direct georeferencing accuracy of unmanned aerial vehicle (UAV) images with real-time kinematic (RTK) measurements is a concerned topic in the community of photogrammetry. This study assesses the capabilities of a multi-rotor platform equipped with RTK technology, specifically a DJI Phantom 4 RTK UAV, for robust direct georeferencing. The UAV surveyed a square and a building at Wuhan University to assess the accuracy and spatial consistency of direct georeferencing in close-range photography. We tested checkpoint errors under various ground control points (GCPs) configurations. The results show that without GCP, an analysis of 71 spatially distributed checkpoints produced a root mean square error (RMSE) of 5.58 cm in the Z direction. This finding indicates that RTK-equipped UAVs can achieve acceptable error margins even without using GCPs, thereby fulfilling the precision requirements for large-scale mapping.

The Façade Opening Rate (FOR) reflects a building’s capacity to withstand seismic loads, serving as a crucial foundation for seismic risk assessment and management. However, FOR data are often outdated or nonexistent in rural areas, which are particularly vulnerable to earthquake damage. This paper proposes a practical framework for estimating FORs from real-scene 3D models derived from UAV photogrammetry. The framework begins by extracting individual buildings from 3D models using annotated roof outlines. The known edges of the roof outline are then utilized to sample and generate orthogonally projected front-view images for each building façade, enabling undistorted area measurements. Next, a modified convolutional neural network is employed to automatically extract opening areas (windows and doors) from the front-view façade images. To enhance the accuracy of opening area extraction, a vanishing point correction method is applied to open-source street-view samples, aligning their style with the front-view images and leveraging street-view-labeled samples. Finally, the FOR is estimated for each building by extracting the façade wall area through simple spatial analysis. Results on two test datasets show that the proposed method achieves high accuracy in FOR estimation. Regarding the mean relative error (MRE), a critical evaluation metric which measures the relative difference between the estimated FOR and its ground truth, the proposed method outperforms the closest baseline by 5%. Moreover, on the façade images we generated, the MRE of our method was improve by 1% and 2% compared to state-of-the-art segmentation methods. These results demonstrate the effectiveness of our framework in accurately estimating FORs and highlight its potential for improving seismic risk assessment in rural areas.

This study proposes a three-dimensional (3D) deformation estimation framework based on the integration of shape and texture information for real-scene 3D model matching, effectively addressing the issue of deformation assessment in large-scale geological landslide areas. By extracting and merging the texture and shape features of matched points, correspondences between points in multi-temporal real-scene 3D models are established, resolving the difficulties faced by existing methods in achieving robust and high-precision 3D point matching over landslide areas. To ensure the complete coverage of the geological disaster area while enhancing computational efficiency during deformation estimation, a voxel-based thinning method to generate interest points is proposed. The effectiveness of the proposed method is validated through tests on a dataset from the Lijie north hill geological landslide area in Gansu Province, China. Experimental results demonstrate that the proposed method significantly outperforms existing classic and advanced methods in terms of matching accuracy metrics, and the accuracy of our deformation estimates is close to the actual measurements obtained from GNSS stations, with an average error of only 2.2 cm.