Building reconstruction from LIDAR data and aerial imagery

Liang Chien Chen, Tee Ann Teo, Jiann Yeou Rau, Jin King Liu, Wei Chen Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

46 Citations (Scopus)

Abstract

This paper presents a scheme for building detection and reconstruction by merging LIDAR data and aerial imagery. In the building detection part, a region-based segmentation and object-based classification are integrated. In the building reconstruction, we analyze the coplanarity of the LIDAR point clouds to shape roofs. The accurate positions of the building walls are then determined by integrating the edges extracted from aerial imagery and the plane derived from LIDAR point clouds. The three dimensional building edges are thus used to reconstruct the building models. In the reconstruction, a patented method SMS (Split-Merge-Shape) is incorporated. Having the advantages of high reliability and flexibility, the SMS method provides stable solution even when those 3D building lines are broken. LIDAR data acquired by Leica ALS 40 and aerial images were used in the validation. Experimental results indicate that the successful rate for building detecition is higher that 81%. The positioning for buildings may reach sub-meter accuracy.

Original languageEnglish
Title of host publication25th Anniversary IGARSS 2005
Subtitle of host publicationIEEE International Geoscience and Remote Sensing Symposium
Pages2846-2849
Number of pages4
DOIs
Publication statusPublished - 2005 Dec 1
Event2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005 - Seoul, Korea, Republic of
Duration: 2005 Jul 252005 Jul 29

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume4

Other

Other2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005
CountryKorea, Republic of
CitySeoul
Period05-07-2505-07-29

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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