A hybrid approach to extraction and refinement of building footprints from airborne lidar data

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Huang, Hai; Sester, Monika: A hybrid approach to extraction and refinement of building footprints from airborne lidar data. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: [Geospatial Data Infrastructure: From Data Acquisition And Updating To Smarter Services] 38-4 (2011), Nr. W25, S. 153-158. DOI: https://doi.org/10.5194/isprsarchives-XXXVIII-4-W25-153-2011

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Sum total of downloads: 175

This work presents a combined bottom-up and top-down approach to extraction and refinement of building footprints from airborne LIDAR data. Building footprints are interesting for many applications in urban planning. The cadastral maps, however, may be limited for certain areas or not be updated frequently. Airborne laser scanning data is therefore considered by many people in the last decade as an important alternative data for change detection and update of building footprints. Laser scanning data of city scenes, however, often shows noise and incompleteness because of, e.g., the clutter by vegetation and the reflection of windows/waterlogged depressions on the roof. Results of the bottom-up detection may thus be limited to incomplete or irregular polygons. We employ 3D Hough transform to detect the building points. An improved joint multiple-plane detection scheme is proposed to find and label the laser points on multiple roof facets synchronously. The bottom-up processing provides not only a rough point segmentation but also additional 3D information, e.g., roof heights and horizontal ridges. Using these as priors, a top-down reconstruction is conducted via generative models. We consider the building footprint as an assembly of regular primitives. A statistical search by means of Reversible Jump Markov Chain Monte Carlo and Maximum A Posteriori estimation is implemented to find the optimal configuration of the footprint. By these means a robust and plausible reconstruction is guaranteed. First results on point clouds with various resolutions show the potential of this approach.
License of this version: CC BY 3.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2011
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 107 61.14%
2 image of flag of United States United States 15 8.57%
3 image of flag of China China 13 7.43%
4 image of flag of India India 8 4.57%
5 image of flag of Turkey Turkey 5 2.86%
6 image of flag of Malaysia Malaysia 3 1.71%
7 image of flag of United Kingdom United Kingdom 3 1.71%
8 image of flag of Indonesia Indonesia 2 1.14%
9 image of flag of Czech Republic Czech Republic 2 1.14%
10 image of flag of Austria Austria 2 1.14%
    other countries 15 8.57%

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