Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer

Abstract

The goal of the U.S. Geological Survey’s (USGS) 3D Elevation Program (3DEP) is to facilitate the acquisition of nationwide lidar data. Although data meet USGS lidar specifications, some point cloud tiles include noisy and incorrectly classified points. The enhanced accuracy of classified point clouds can improve support for many downstream applications such as hydrologic analysis, urban planning, and forest management. Despite noisy and incorrectly classified points, the current 3DEP classification specifications result in data that can be useful for Digital Terrain Model (DTM) extraction; however, the quality of the classification application can be improved to match state-of-the-art capabilities. Deep Learning (DL)-based approaches have been developed with outstanding performance for point cloud classification. This study will utilize the proven DL technologies to prepare for developing a user-friendly open-source toolkit that would automate classification to refine and enrich the results of existing and future 3DEP data.