Detection of collapsed buildings by classifying segmented lidar data: ISPRS Calgary Workshop, held on 29-31 August 2011, Calgary, Canada
https://www.researchgate.net/publication/264085078
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| Format: | Article |
| Language: | English |
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2016
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| Online Access: | http://hdl.handle.net/11408/1005 |
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| author | Elberink, Sander Oude Shoko, Moreblessings Fathi, Seyed Abdolmajid Rutzinger, Martin |
| author_facet | Elberink, Sander Oude Shoko, Moreblessings Fathi, Seyed Abdolmajid Rutzinger, Martin |
| author_sort | Elberink, Sander Oude |
| collection | DSpace |
| description | https://www.researchgate.net/publication/264085078 |
| format | Article |
| id | ir-11408-1005 |
| institution | My University |
| language | English |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ir-11408-10052022-06-27T13:49:05Z Detection of collapsed buildings by classifying segmented lidar data: ISPRS Calgary Workshop, held on 29-31 August 2011, Calgary, Canada Elberink, Sander Oude Shoko, Moreblessings Fathi, Seyed Abdolmajid Rutzinger, Martin Supervised classification, maximum entropy modelling, rule based classification, airborne laser scanner data, segmentation, object-based point cloud analysis https://www.researchgate.net/publication/264085078 Rapid mapping of damaged regions and individual buildings is essential for efficient crisis management. Airborne laser scanner (ALS) data is potentially able to deliver accurate information on the 3D structures in a damaged region. In this paper we describe two different strategies how to process ALS point clouds in order to detect collapsed buildings automatically. Our aim is to detect collapsed buildings using post event data only. The first step in the workflow is the segmentation of the point cloud detecting planar regions. Next, various attributes are calculated for each segment. The detection of damaged buildings is based on the values of these attributes. Two different classification strategies have been applied in order to test whether the chosen strategy is capable of detecting collapsed buildings. The results of the classification are analysed and assessed for accuracy against a reference map in order to validate the quality of the rules derived. Classification results have been achieved with accuracy measures from 60-85% completeness and correctness. It is shown that not only the classification strategy influences the accuracy measures; also the validation methodology, including the type and accuracy of the reference data, plays a major role. 2016-04-24T16:21:09Z 2016-04-24T16:21:09Z 2011 Article http://hdl.handle.net/11408/1005 en open The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spellingShingle | Supervised classification, maximum entropy modelling, rule based classification, airborne laser scanner data, segmentation, object-based point cloud analysis Elberink, Sander Oude Shoko, Moreblessings Fathi, Seyed Abdolmajid Rutzinger, Martin Detection of collapsed buildings by classifying segmented lidar data: ISPRS Calgary Workshop, held on 29-31 August 2011, Calgary, Canada |
| title | Detection of collapsed buildings by classifying segmented lidar data: ISPRS Calgary Workshop, held on 29-31 August 2011, Calgary, Canada |
| title_full | Detection of collapsed buildings by classifying segmented lidar data: ISPRS Calgary Workshop, held on 29-31 August 2011, Calgary, Canada |
| title_fullStr | Detection of collapsed buildings by classifying segmented lidar data: ISPRS Calgary Workshop, held on 29-31 August 2011, Calgary, Canada |
| title_full_unstemmed | Detection of collapsed buildings by classifying segmented lidar data: ISPRS Calgary Workshop, held on 29-31 August 2011, Calgary, Canada |
| title_short | Detection of collapsed buildings by classifying segmented lidar data: ISPRS Calgary Workshop, held on 29-31 August 2011, Calgary, Canada |
| title_sort | detection of collapsed buildings by classifying segmented lidar data: isprs calgary workshop, held on 29-31 august 2011, calgary, canada |
| topic | Supervised classification, maximum entropy modelling, rule based classification, airborne laser scanner data, segmentation, object-based point cloud analysis |
| url | http://hdl.handle.net/11408/1005 |
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