A Novel Approach for Digital Building Modeling Using LiDAR and Image Data Integration

Accurate and realistic 3D building reconstruction remains a fundamental challenge in the fields of photogrammetry and remote sensing. This study introduces an integrated methodology that enhances building model generation by fusing airborne LiDAR and photogrammetric data, capitalizing on the complementary strengths of both sources. The proposed approach involves feature extraction, segmentation, and 3D model reconstruction based on the identification of planar surfaces and boundary primitives within the fused point cloud. Data were acquired over the Tora Cement Factory in Cairo, Egypt, using DJI Zenmuse L1 (LiDAR) and P1 (RGB) sensors. To ensure precise georeferencing and validation, twelve ground control points (GCPs) were measured using the Trimble R10 GNSS system. The final 3D building model derived from the fused dataset achieved a root mean square error (RMSE) of 7.4 cm, significantly outperforming models generated from LiDAR-only data (11.2 cm) and photogrammetric-only data (14.6 cm). These findings demonstrate the effectiveness of the proposed fusion strategy in improving geometric accuracy and structural completeness, offering a robust solution for high-quality 3D building reconstruction from multi-source aerial data.