Summary
Density-based clustering allows the identification of objects from unstructured data. The DBSCAN and OPTICS algorithms allow clustering and classification of remotely-sensed points into objects; however, current implementations have been unable to handle the data volume produced by LiDAR (Light Detection And Ranging). Using modified kd-trees as a spatial index allows for increased scalability.