Comprehensive approach for building outline extraction from LiDAR data with accent to a sparse laser scanning point cloud

Petr Hofman,Markéta Potůčková

doi:10.14311/gi.16.1.6

Abstract

<em>The method of building outline extraction based on segmentation of airborne laser scanning data is proposed and tested on a dataset comprising 1,400 buildings typical for residential and industrial urban areas. The algorithm starts with setting a special threshold to separate building from bare earth points and low objects. Next, local planes are fitted to each point using RANSAC and further refined by least squares adjustment. A normal vector is assigned to each point. Similarities among normal vectors are evaluated in order to assemble planar or curved roof segments. Finally, building outlines are formed from detected segments using the a-shapes algorithm and further regularized. The extracted outlines were compared with reference polygons manually derived from the processed laser scanning point cloud and orthoimages. Area-based evaluation of accuracy of the proposed method revealed completeness and correctness of 87 % and 97 %, respectively, for the test dataset. The influence of parameters like number of points per roof segment, complexity of the roof structure, roof type, and overlap with vegetation on accuracy was evaluated and discussed.</em>

Highlights

Increasing demand on 3D information and variety of its applications ranging from architecture, engineering, real estate, telecommunication or tourism and development in technologies like airborne laser scanning (ALS) and multi image processing have triggered research on algorithms for derivation of 3D building models from point clouds and on related issues such as accuracy assessment, transferability of the algorithms to different datasets, fusion of LiDAR and image data
A plane fitting to its neighbourhood and a corresponding normal vector are assigned to each point on artificial and natural objects
The object-based approach only shows whether the building was roughly detected; compared to the area-based evaluation, it does not express the geometric similarity between the reference and extracted building outlines

Summary

Introduction

Increasing demand on 3D information and variety of its applications ranging from architecture, engineering, real estate, telecommunication or tourism and development in technologies like airborne laser scanning (ALS) and multi image processing have triggered research on algorithms for derivation of 3D building models from point clouds and on related issues such as accuracy assessment, transferability of the algorithms to different datasets, fusion of LiDAR and image data. A comprehensive review of about 100 papers dealing with building extraction from ALS data published in the last two decades, challenges and possible research trends can be found in [17]. Increasing point density increases the accuracy of extracted building outlines [17]. Sparser LiDAR point clouds are acquired for nationwide mapping and their use for building extraction and modelling has been investigated Sparser LiDAR point clouds are acquired for nationwide mapping and their use for building extraction and modelling has been investigated (e.g. [14, 4, 2])

Methods

Results

Conclusion