Abstract
This paper presents a scheme to detect building regions, followed by a reconstruction procedure. Airborne LIDAR data and aerial imagery are integrated in the proposed scheme. In light of the different buildings, we target the ones with straight and curvilinear boundaries. In the detection stage, a region-based segmentation and object-based classification are integrated. In the building reconstruction, we perform an edge detection to obtain the initial building lines from the rasterized LIDAR data. The accurate arcs and straight lines are then obtained in the image space. By employing the roof analysis, we determine the three dimensional building structure lines. Finally, the Split-Merge-Shape method is applied to generate the building models. Experimental results indicate that the success rate of the building detection reaches 91%. Among the successfully detected buildings, 90% of the buildings are fully or partially reconstructed. The planimetric accuracy of the building boundaries is better than 0.8m, while the shaping error of reconstructed roofs in height is 0.14 m.
| Original language | English |
|---|---|
| Title of host publication | Advances in Image and Video Technology - First Pacific Rim Symposium, PSIVT 2006, Proceedings |
| Pages | 24-33 |
| Number of pages | 10 |
| DOIs | |
| Publication status | Published - 2006 Dec 1 |
| Event | 1st Pacific Rim Symposium on Image and Video Technology, PSIVT 2006 - Hsinchu, Taiwan Duration: 2006 Dec 10 → 2006 Dec 13 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 4319 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 1st Pacific Rim Symposium on Image and Video Technology, PSIVT 2006 |
|---|---|
| Country | Taiwan |
| City | Hsinchu |
| Period | 06-12-10 → 06-12-13 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Reconstruction of building models with curvilinear boundaries from laser scanner and aerial imagery. / Chen, Liang Chien; Teo, Tee Ann; Hsieh, Chi Heng; Rau, Jiann-Yeou.
Advances in Image and Video Technology - First Pacific Rim Symposium, PSIVT 2006, Proceedings. 2006. p. 24-33 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4319 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Reconstruction of building models with curvilinear boundaries from laser scanner and aerial imagery
AU - Chen, Liang Chien
AU - Teo, Tee Ann
AU - Hsieh, Chi Heng
AU - Rau, Jiann-Yeou
PY - 2006/12/1
Y1 - 2006/12/1
N2 - This paper presents a scheme to detect building regions, followed by a reconstruction procedure. Airborne LIDAR data and aerial imagery are integrated in the proposed scheme. In light of the different buildings, we target the ones with straight and curvilinear boundaries. In the detection stage, a region-based segmentation and object-based classification are integrated. In the building reconstruction, we perform an edge detection to obtain the initial building lines from the rasterized LIDAR data. The accurate arcs and straight lines are then obtained in the image space. By employing the roof analysis, we determine the three dimensional building structure lines. Finally, the Split-Merge-Shape method is applied to generate the building models. Experimental results indicate that the success rate of the building detection reaches 91%. Among the successfully detected buildings, 90% of the buildings are fully or partially reconstructed. The planimetric accuracy of the building boundaries is better than 0.8m, while the shaping error of reconstructed roofs in height is 0.14 m.
AB - This paper presents a scheme to detect building regions, followed by a reconstruction procedure. Airborne LIDAR data and aerial imagery are integrated in the proposed scheme. In light of the different buildings, we target the ones with straight and curvilinear boundaries. In the detection stage, a region-based segmentation and object-based classification are integrated. In the building reconstruction, we perform an edge detection to obtain the initial building lines from the rasterized LIDAR data. The accurate arcs and straight lines are then obtained in the image space. By employing the roof analysis, we determine the three dimensional building structure lines. Finally, the Split-Merge-Shape method is applied to generate the building models. Experimental results indicate that the success rate of the building detection reaches 91%. Among the successfully detected buildings, 90% of the buildings are fully or partially reconstructed. The planimetric accuracy of the building boundaries is better than 0.8m, while the shaping error of reconstructed roofs in height is 0.14 m.
UR - http://www.scopus.com/inward/record.url?scp=70350292685&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350292685&partnerID=8YFLogxK
U2 - 10.1007/11949534-3
DO - 10.1007/11949534-3
M3 - Conference contribution
SN - 354068297X
SN - 9783540682974
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 24
EP - 33
BT - Advances in Image and Video Technology - First Pacific Rim Symposium, PSIVT 2006, Proceedings
ER -