AI Indexing of Aerial LiDAR Point Cloud for Efficient Query

In the era of information revolution, with data being the fuel of AI and analytics, efficient information extraction from LiDAR point clouds becomes indispensable for solving real-world problems and aiding decision-making in geospatial domain. Despite having geometric richness, the massive LiDAR point clouds are not only computationally demanding but also lack inherent semantics. The lack of semantics in LiDAR constrains effective data analysis. This paper presents a novel workflow by incorporating Deep Learning derived embeddings as attributes in the geospatial database for the spatio-semantic querying on Aerial LiDAR point clouds. This work leverages AI-based indexing, such as IVFFlat(Inverted File Index with Flat Quantization) on LiDAR point clouds for fast retrieval of queries. The pgPointCloud and pgVector extensions of PostgreSQL aid in importing point clouds into the database and performing similarity-based query retrieval on the embedding space of the point clouds. The methodology developed in this paper explores how semantic embeddings can handle inadequate semantics of point clouds by enabling direct and complex 3D intelligent queries within the database environment, thereby overcoming the limitations of traditional LiDAR representations. Few queries presented in this paper highlight the applications of this proposed framework in individual tree detection, tree species identification, utility management, urban planning and anomaly detection.