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Cited by 38Automated Road and Building Extraction3D Modeling in Geospatial Applications3D Surveying and Cultural Heritage

The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.

Optimization of OpenStreetMap Building Footprints Based on Semantic Information of Oblique UAV Images

Open access
Zhuo, Xiangyu, Fraundorfer, Friedrich, Kurz, Franz, Reinartz, Peter·2018·Remote Sensing
DOI 10.3390/rs10040624
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Abstract

Building footprint information is vital for 3D building modeling. Traditionally, in remote sensing, building footprints are extracted and delineated from aerial imagery and/or LiDAR point cloud. Taking a different approach, this paper is dedicated to the optimization of OpenStreetMap (OSM) building footprints exploiting the contour information, which is derived from deep learning-based semantic segmentation of oblique images acquired by the Unmanned Aerial Vehicle (UAV). First, a simplified 3D building model of Level of Detail 1 (LoD 1) is initialized using the footprint information from OSM and the elevation information from Digital Surface Model (DSM). In parallel, a deep neural network for pixel-wise semantic image segmentation is trained in order to extract the building boundaries as contour evidence. Subsequently, an optimization integrating the contour evidence from multi-view images as a constraint results in a refined 3D building model with optimized footprints and height. Our method is leveraged to optimize OSM building footprints for four datasets with different building types, demonstrating robust performance for both individual buildings and multiple buildings regardless of image resolution. Finally, we compare our result with reference data from German Authority Topographic-Cartographic Information System (ATKIS). Quantitative and qualitative evaluations reveal that the original OSM building footprints have large offset, but can be significantly improved from meter level to decimeter level after optimization.

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Crossref · 10.3390/rs10040624

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Citations

Cites

Research works printed but not linked: 3

  1. Seyfert, 1998, Quality assurance for 2.5-D building data of the ATKIS DLM 25/2, Int. Arch. Photogramm. Remote Sens., 32, 411
  2. Shih, F.Y. (2009). Image Processing and Mathematical Morphology: Fundamentals and Applications, CRC Press.
  3. Press, W.H., Flannery, B.P., Teukolsky, S.A., and Vetterling, W.T. (1989). Numerical Recipes, Cambridge University Press.

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Metadata

Authors
Zhuo, X., Fraundorfer, F., Kurz, F., & Reinartz, P.
Affiliations
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
Corresponding author
Zhuo, Xiangyu
ORCID
Venue
Remote Sensing
Publisher
MDPI AG
Published
2018-04-18
Volume
10
Issue
4
Pages
624
ISSN
2072-4292 (ISSN-L)
DOI
10.3390/rs10040624
OpenAlex
W2799982666
Cited by
38
References
37
Citations by year
2026: 2 · 2025: 5 · 2024: 4 · 2023: 7 · 2022: 4 · 2021: 6 · 2020: 7 · 2019: 2 · 2018: 1
FWCI
3.20
Type
journal article
Language
en
Open access
Licence
Locations
  • Remote Sensing (publishedVersion, cc-by)
  • elib (German Aerospace Center) (acceptedVersion)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Automated Road and Building Extraction
  • Physical Sciences › Engineering › Building and Construction › 3D Modeling in Geospatial Applications
  • Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
Keywords
OpenStreetMap, semantic segmentation, 3D building modeling, building contour extraction, multi-view image fusion, building height estimation, Level of Detail 1, digital surface model

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