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 accessAbstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Indeed, some of the pioneering works explored other interesting applications of semantic segmentation, such as the semantic segmentation of large-scale 3D scenes (Zhang et al., 2018a), and the extraction of building footprints and building heights (Zhuo et al., 2018), which hints at expanding the applications of DCNN-based semantic segmentation in the future.”
From Deep learning in remote sensing applications: A meta-analysis and review (2019) · cites this paper as Zhuo et al., 2018
Citations
Cites
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Powell, M. J. D. (1964) · The Computer Journal · doi:10.1093/comjnl/7.2.155 · Cited by 4,632 · cited 1×
“We solve the minimization problem using a modification of Powell’s method [35,36], which performs sequential one-dimensional minimizations along each vector of the directions set.”
MethodConditional Random Fields as Recurrent Neural Networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.179 · Cited by 2,386 · cited 1×
“To solve this problem, we plug in a Conditional Random Field (CRF) represented as Recurrent Neural Network (CRFasRNN) [30,31] at the end of the FCN, which combines the strengths of both the CNN and CRF based graphical model in one unified framework.”
MethodDistance transformations in digital images
Borgefors, G. (1986) · Computer Vision Graphics and Image Processing · doi:10.1016/s0734-189x(86)80047-0 · Cited by 1,922
Quality assessment for building footprints data on OpenStreetMap
Fan, H., Zipf, A., Fu, Q., et al. (2014) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2013.867495 · Cited by 425
Creating Large-Scale City Models from 3D-Point Clouds: A Robust Approach with Hybrid Representation
Lafarge, F., & Mallet, C. (2012) · International Journal of Computer Vision · doi:10.1007/s11263-012-0517-8 · Cited by 325
Parameter-free ground filtering of LiDAR data for automatic DTM generation
Mongus, D., & Žalik, B. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2011.10.002 · Cited by 239
Higher Order Conditional Random Fields in Deep Neural Networks
Arnab, A., Jayasumana, S., Zheng, S., & Torr, P. H. S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46475-6_33 · Cited by 205
Building detection from aerial images using invariant color features and shadow information
Sırmaçek, B., & Ünsalan, C. (2008) · International Symposium on Computer and Information Sciences · doi:10.1109/iscis.2008.4717854 · Cited by 161
Research works printed but not linked: 3
- Seyfert, 1998, Quality assurance for 2.5-D building data of the ATKIS DLM 25/2, Int. Arch. Photogramm. Remote Sens., 32, 411
- Shih, F.Y. (2009). Image Processing and Mathematical Morphology: Fundamentals and Applications, CRC Press.
- Press, W.H., Flannery, B.P., Teukolsky, S.A., and Vetterling, W.T. (1989). Numerical Recipes, Cambridge University Press.
Cited by
Sorted by citations
Deep learning in remote sensing applications: A meta-analysis and review
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456
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
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2018-04-18
- Volume
- 10
- Issue
- 4
- Pages
- 624
- ISSN
- 2072-4292 (ISSN-L)
- 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
- gold
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Optimization of OpenStreetMap Building Footprints Based on Semantic Information of Oblique UAV Images. https://app.cochat.ai/papers/10.3390/rs10040624
- The paper
- Zhuo, X., Fraundorfer, F., Kurz, F., & Reinartz, P. (2018). Optimization of OpenStreetMap Building Footprints Based on Semantic Information of Oblique UAV Images. Remote Sensing. https://doi.org/10.3390/rs10040624
