Topological elevation connection analysis for 3D line detection from dense airborne LIDAR data

Abstract

This paper proposes a kernel-based strategy to analyze the distribution of local point clouds and detect 3D lines from dense airborne LIght Detection and Ranging (LIDAR) data. The proposed method, namely topological elevation connection (TEC) analysis, employs geometric criteria with an automatic procedure instead of threshold operation. The focused targets include flat roofs with parapets, step roofs, hipped roofs, and gable roofs. By comparing with reference data, the accuracy between the detected lines and reference data can reach within 0.2 m. Three-dimensional boundaries and structure lines are essential for building modeling and data registration. Many related works have focused on the collection of co-planar LIDAR points to calculate intersection lines with optimized thresholds. In addition, they did not focus on parapets because of the limitations of developed assumptions and the criteria used. It is crucial to analyze the local point distribution for the automation improvement of detection processes. The major objective is to automatically detect 3D lines using the TEC analysis instead of threshold selection. The interesting targets include building boundaries and a variety of rooftops. The core concept of TEC is that the local relief along the alignment must be smoother than the local relief across the alignment. All consecutive point clouds, which belong to one line, must be sufficient for progressive elevations along the alignment and the linear shape. In the validation, the detected results are compared with reference data. The comparisons indicate that the proposed scheme can reach a detection accuracy of 0.2 m and provide a higher degree of modeling automation for varied building types.