Fusion of RGB and LiDAR Modalities for Building Footprint Extraction Using High-Resolution Aerial Imagery.
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| Title: | Fusion of RGB and LiDAR Modalities for Building Footprint Extraction Using High-Resolution Aerial Imagery. |
|---|---|
| Authors: | Serbán, Norbert1 (AUTHOR) serban.norbert@inf.unideb.hu, Enyedi, Péter2 (AUTHOR), Burai, Péter3 (AUTHOR), Harangi, Balázs1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p2049. 21p. |
| Subjects: | Point cloud, Image segmentation, Deep learning, Aerial photographs, Land use mapping |
| Abstract: | Highlights: What are the main findings? The proposed RGB–LiDAR fusion model, Point U-Net, significantly outperforms traditional single-source (RGB-only or LiDAR-only) and ensemble segmentation methods in building detection accuracy. Integrating 2D image features with 3D point cloud information at multiple decoding levels leads to more detailed and reliable semantic segmentation results for urban and environmental mapping tasks. What are the implications of the main findings? The improved accuracy of RGB–LiDAR fusion models suggests that combining complementary data sources can greatly enhance the reliability and accuracy of building detection, benefiting applications such as urban planning, infrastructure monitoring, and environmental analysis. This approach demonstrates the potential for developing more advanced multimodal deep learning architectures, encouraging further research into data fusion techniques for other remote sensing and geospatial analysis tasks. In this paper, a novel approach is presented for fusing RGB and LiDAR inputs for semantic segmentation. Accurate building detection is required for various scenarios such as urban planning or environmental monitoring. The two main sources for accurate building segmentation are either RGB aerial images or LiDAR point clouds covering the selected area. Each of these sources has its own well-known techniques for segmentation; however, for the combination of the input, there are not many architectures available, and extracting different features from the two different fields can result in an enhanced segmentation map. The authors of this article created a semantic segmentation model that uses both the aerial RGB image and the LiDAR point cloud as its input. The network first takes the point cloud and forwards the processed projection to a modified U-Net-based architecture, which fuses the extracted features of the 3D input with the extracted information of the 2D input on each level of the decoding. To train and test the presented model, the authors used a dataset containing more than 3000 images and their corresponding 3D point clouds of three different areas from Hungary. As is also presented in this paper, this approach provides significantly better results than the traditional RGB, Point Cloud segmentation models, and their ensembles in terms of segmentation accuracy. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194915182 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Fusion of RGB and LiDAR Modalities for Building Footprint Extraction Using High-Resolution Aerial Imagery. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Serbán%2C+Norbert%22">Serbán, Norbert</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> serban.norbert@inf.unideb.hu</i><br /><searchLink fieldCode="AR" term="%22Enyedi%2C+Péter%22">Enyedi, Péter</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Burai%2C+Péter%22">Burai, Péter</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Harangi%2C+Balázs%22">Harangi, Balázs</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p2049. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Point+cloud%22">Point cloud</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Aerial+photographs%22">Aerial photographs</searchLink><br /><searchLink fieldCode="DE" term="%22Land+use+mapping%22">Land use mapping</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? The proposed RGB–LiDAR fusion model, Point U-Net, significantly outperforms traditional single-source (RGB-only or LiDAR-only) and ensemble segmentation methods in building detection accuracy. Integrating 2D image features with 3D point cloud information at multiple decoding levels leads to more detailed and reliable semantic segmentation results for urban and environmental mapping tasks. What are the implications of the main findings? The improved accuracy of RGB–LiDAR fusion models suggests that combining complementary data sources can greatly enhance the reliability and accuracy of building detection, benefiting applications such as urban planning, infrastructure monitoring, and environmental analysis. This approach demonstrates the potential for developing more advanced multimodal deep learning architectures, encouraging further research into data fusion techniques for other remote sensing and geospatial analysis tasks. In this paper, a novel approach is presented for fusing RGB and LiDAR inputs for semantic segmentation. Accurate building detection is required for various scenarios such as urban planning or environmental monitoring. The two main sources for accurate building segmentation are either RGB aerial images or LiDAR point clouds covering the selected area. Each of these sources has its own well-known techniques for segmentation; however, for the combination of the input, there are not many architectures available, and extracting different features from the two different fields can result in an enhanced segmentation map. The authors of this article created a semantic segmentation model that uses both the aerial RGB image and the LiDAR point cloud as its input. The network first takes the point cloud and forwards the processed projection to a modified U-Net-based architecture, which fuses the extracted features of the 3D input with the extracted information of the 2D input on each level of the decoding. To train and test the presented model, the authors used a dataset containing more than 3000 images and their corresponding 3D point clouds of three different areas from Hungary. As is also presented in this paper, this approach provides significantly better results than the traditional RGB, Point Cloud segmentation models, and their ensembles in terms of segmentation accuracy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18122049 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 2049 Subjects: – SubjectFull: Point cloud Type: general – SubjectFull: Image segmentation Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Aerial photographs Type: general – SubjectFull: Land use mapping Type: general Titles: – TitleFull: Fusion of RGB and LiDAR Modalities for Building Footprint Extraction Using High-Resolution Aerial Imagery. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Serbán, Norbert – PersonEntity: Name: NameFull: Enyedi, Péter – PersonEntity: Name: NameFull: Burai, Péter – PersonEntity: Name: NameFull: Harangi, Balázs IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 12 Titles: – TitleFull: Remote Sensing Type: main |
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