Data & Code

Semantic 3D city models serve as a crucial geospatial framework, foundation, and vital spatial data source that underpins various smart city applications. While oblique photogrammetry has made significant progress in 3D construction, the models it generates continue to encounter issues such as high data volume, challenges in monomerization, and numerous geometric and texture defects. Particularly the use of intricate algorithms often leads to inefficiencies and high expenditures in city-scale modeling when introducing semantic information. Hence, this study proposes the SemCity-Gaussian method based on 3D Gaussian Splatting. It aims to perform high-precision 3D scene reconstruction and fast, high-quality rendering of typical urban geographic features with great significance while preserving the geographic semantics. Experimental results demonstrated that the models generated by the SemCity-Gaussian method exhibit outstanding performance in semantic segmentation and geometric accuracy, with an overall mean Intersection over Union (mIoU) exceeding 80% and a mean geometric error of only 0.008 meters compared to ground truth model obtained by 3D laser scanning. The SemCity-Gaussian method can generate semantic 3D models rapidly, accurately, and with low expenditure, providing a more efficient and intelligent solution for building Internet + 3D GIS platforms and promoting the construction of smart cities.