Enhancing Urban Building Modeling Accuracy with Drone Imagery and Ground Control Points Using SFM/ MVS Techniques

Abstract

The use of unmanned aerial vehicles (UAVs) for aerial photography has become increasingly popular in various fields, including engineering, urban planning, environmental impact studies, and monitoring of transportation lines, power supplies, and other military applications. One of the main challenges in using drone imagery to model urban buildings is achieving high accuracy in generating 3D models. This paper focuses on improving the accuracy of urban building modelling using drone imagery, structure from motion, and multi-view stereo techniques used in computer vision, augmented virtual reality, geo-science, photography, and aerial photography to create 3D models from pairs of images. The study uses a UAV with a DJI Mavic 2 Pro drone 4K sophisticated camera for up to 31 minutes of flight time. Data collection time at different angles and specific heights while incorporating ground control points to enhance the accuracy of the generated point clouds. A specific location inside Al-Nahrain University was chosen as a fieldwork model, where the images captured by the drone were processed using Agisoft Metashape Pro software to create detailed 3D models for the buildings depending on the Structure form Motion (SfM) technique. The results demonstrate the efficient integration of Ground Control Points (GCP) combined with advanced processing techniques that enhance the model accuracy, achieving highly accurate GCP demonstrating error margins as low as 0.1% based on drone-derived data for urban buildings.

Keywords

• 3D Building Models, UAV Image, Multi-view Stereo (MVS), Point Cloud Analysis, Texture Mapping