Automated residential building detection from airborne LiDAR data with deep neural networks

Zixiang Zhou, Jie Gong

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Detection of building objects in airborne LiDAR data is an essential task in many types of geospatial data applications such as urban reconstruction and damage assessment. Traditional approaches used in building detection often rely on shape primitives that can be detected by 2D/3D computer vision techniques. These approaches require carefully engineered features which tend to be specific to building types. Furthermore, these approaches are often computationally expensive with the increase of data size. In this paper, we propose a novel approach that employs a deep neural network to recognize and extract residential building objects in airborne LiDAR data. This proposed approach does not require any pre-defined geometric or texture features, and it is applicable to airborne LiDAR data sets with varied point densities and with damaged building objects. The latter makes our approach particularly useful in damage assessment applications. The research results show that the proposed approach is capable of achieving the state-of-the-art accuracy in building detection in these different types of point cloud data sets.

Original languageEnglish (US)
Pages (from-to)229-241
Number of pages13
JournalAdvanced Engineering Informatics
StatePublished - Apr 2018

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Artificial Intelligence


  • Airborne LiDAR
  • Building detection
  • Deep learning


Dive into the research topics of 'Automated residential building detection from airborne LiDAR data with deep neural networks'. Together they form a unique fingerprint.

Cite this