Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 256-271 |
| Seitenumfang | 16 |
| Fachzeitschrift | ISPRS Journal of Photogrammetry and Remote Sensing |
| Jahrgang | 93 |
| Frühes Online-Datum | 16 Nov. 2013 |
| Publikationsstatus | Veröffentlicht - Juli 2014 |
Abstract
For more than two decades, many efforts have been made to develop methods for extracting urban objects from data acquired by airborne sensors. In order to make the results of such algorithms more comparable, benchmarking data sets are of paramount importance. Such a data set, consisting of airborne image and laserscanner data, has been made available to the scientific community by ISPRS WGIII/4. Researchers were encouraged to submit their results of urban object detection and 3D building reconstruction, which were evaluated based on reference data. This paper presents the outcomes of the evaluation for building detection, tree detection, and 3D building reconstruction. The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of-the-art methods.
ASJC Scopus Sachgebiete
- Physik und Astronomie (insg.)
- Atom- und Molekularphysik sowie Optik
- Ingenieurwesen (insg.)
- Ingenieurwesen (sonstige)
- Informatik (insg.)
- Angewandte Informatik
- Erdkunde und Planetologie (insg.)
- Computer in den Geowissenschaften
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in: ISPRS Journal of Photogrammetry and Remote Sensing, Jahrgang 93, 07.2014, S. 256-271.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Results of the ISPRS benchmark on urban object detection and 3D building reconstruction
AU - Rottensteiner, Franz
AU - Sohn, Gunho
AU - Gerke, Markus
AU - Wegner, Jan Dirk
AU - Breitkopf, Uwe
AU - Jung, Jaewook
PY - 2014/7
Y1 - 2014/7
N2 - For more than two decades, many efforts have been made to develop methods for extracting urban objects from data acquired by airborne sensors. In order to make the results of such algorithms more comparable, benchmarking data sets are of paramount importance. Such a data set, consisting of airborne image and laserscanner data, has been made available to the scientific community by ISPRS WGIII/4. Researchers were encouraged to submit their results of urban object detection and 3D building reconstruction, which were evaluated based on reference data. This paper presents the outcomes of the evaluation for building detection, tree detection, and 3D building reconstruction. The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of-the-art methods.
AB - For more than two decades, many efforts have been made to develop methods for extracting urban objects from data acquired by airborne sensors. In order to make the results of such algorithms more comparable, benchmarking data sets are of paramount importance. Such a data set, consisting of airborne image and laserscanner data, has been made available to the scientific community by ISPRS WGIII/4. Researchers were encouraged to submit their results of urban object detection and 3D building reconstruction, which were evaluated based on reference data. This paper presents the outcomes of the evaluation for building detection, tree detection, and 3D building reconstruction. The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of-the-art methods.
KW - 3D building reconstruction
KW - Aerial imagery
KW - Automatic object extraction
KW - Benchmarking test
KW - Evaluation
KW - Laser scanning
UR - http://www.scopus.com/inward/record.url?scp=84902353397&partnerID=8YFLogxK
U2 - 10.1016/j.isprsjprs.2013.10.004
DO - 10.1016/j.isprsjprs.2013.10.004
M3 - Article
AN - SCOPUS:84902353397
VL - 93
SP - 256
EP - 271
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
SN - 0924-2716
ER -