Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds

Liang Guo,Xingdong Deng,Huagui He,Yang Liu,Weijun Yang,Guangxin Qiu,Hong Lin

doi:10.1109/access.2021.3102632

Abstract

Point clouds derived from LiDAR (Light Detection and Ranging) and photogrammetry systems are used to extract building footprints in dense urban areas. Two extraction methods based on DSM (Digital Surface Model) images and point clouds are comprehensively evaluated and compared. Firstly, photogrammetric point clouds are generated from aerial images of downtown Guangzhou, China, and compared with corresponding LiDAR point clouds. Then, DSM images are created using these point clouds and a threshold segmentation method is applied for building extraction. Although regularized buildings can be extracted according to the selection of appropriate height thresholds for the LiDAR DSM and photogrammetric DSM, blurry building boundaries exist for results of photogrammetric DSM when high trees are available nearby. LiDAR DSM extraction performs better in terms of Precision, Recall, and $F$ -score metrics. A DoN (Difference of Normals) approach based on point cloud datasets is also quantitatively and qualitatively demonstrated. Our experiments show that when a suitable radius threshold is selected, the method provides satisfactorily normal calculation results and can successfully isolate building roofs from other objects in densely built-up areas. The majority of building extraction results have a precision >0.9 and favorable Recall and $F$ -score results. There is high consistency between photogrammetric and LiDAR point clouds. Although LiDAR provides higher extraction accuracy, photogrammetry is also useful for its more convenient acquisition and higher point cloud densities.

Highlights

The identification and extraction of buildings have become crucial issues in many applications, such as urban basic geodatabase updating, city planning management, disaster assessment, digital mapping, transportation planning, cadastral management, acoustic and energy studies, and telecommunication network design [1]–[4]
There is a higher density in the photogrammetric point clouds, their geometric accuracy is lower than that of LiDAR
Spectral or color information is an advantage of photogrammetric point clouds that is not available in LiDAR data

Summary

Introduction

The identification and extraction of buildings have become crucial issues in many applications, such as urban basic geodatabase updating, city planning management, disaster assessment, digital mapping, transportation planning, cadastral management, acoustic and energy studies, and telecommunication network design [1]–[4]. Collecting building information by field survey is labor-intensive and time-consuming. Building information updates occur slowly compared to the rapid rate of urbanization, especially in developing countries. Various applications, rapid, economical, and accurate building extraction is required. Automatic approaches to building detection have been difficult if not impossible due to scene complexity, incomplete extraction, and sensor dependencies, especially in big cities with dense buildings [9]–[11]

Methods

Results

Discussion

Conclusion