Comparative Analysis of Machine Learning Algorithms for Classification of UAV-based Photogrammetric Cultural Heritage Point Clouds Article Swipe
M. Arkali
,
M. Y. Biyik
,
M. E. Atik
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xlviii-5-w3-2025-17-2025
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xlviii-5-w3-2025-17-2025
Unmanned Aerial Vehicles (UAVs) are being increasingly utilized across different fields because of their ability to deliver quick, cost-effective, and precise spatial information. Innovations in photogrammetry and computer vision techniques, especially Structure from Motion (SfM) and Multi-View Stereo (MVS), have improved the generation of orthoimages, digital surface models, and dense point clouds, rendering UAVs highly efficient for documentation and three-dimensional reconstruction. In studies focused on cultural heritage, UAV-based photogrammetry has emerged as a crucial resource for accurately preserving and representing historical sites with great detail and resolution. In this context, the current study analyzes UAV-acquired point cloud data from the Temple of Hera in Italy and performs a comparative assessment of three machine learning algorithms, Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), for the purpose of semantic segmentation tasks. According to our results, the XGBoost and Random Forests (RF) methods has reached to more than 90% F1 score for all classes, and the SVM method has reached 90% F1 score only for three classes.
Related Topics
Concepts
Point cloud
Photogrammetry
Artificial intelligence
Random forest
Computer science
Support vector machine
Machine learning
Segmentation
Structure from motion
Boosting (machine learning)
Image segmentation
Computer vision
Aerial image
Cultural heritage
Stereo cameras
Machine vision
Documentation
Rendering (computer graphics)
Algorithm
Lidar
Gradient boosting
Point (geometry)
Aerial survey
Stereopsis
Statistical classification
Decision tree
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/isprs-archives-xlviii-5-w3-2025-17-2025
- https://isprs-archives.copernicus.org/articles/XLVIII-5-W3-2025/17/2025/isprs-archives-XLVIII-5-W3-2025-17-2025.pdf
- OA Status
- diamond
- OpenAlex ID
- https://openalex.org/W7105535549
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7105535549Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/isprs-archives-xlviii-5-w3-2025-17-2025Digital Object Identifier
- Title
-
Comparative Analysis of Machine Learning Algorithms for Classification of UAV-based Photogrammetric Cultural Heritage Point CloudsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-12Full publication date if available
- Authors
-
M. Arkali, M. Y. Biyik, M. E. AtikList of authors in order
- Landing page
-
https://doi.org/10.5194/isprs-archives-xlviii-5-w3-2025-17-2025Publisher landing page
- PDF URL
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https://isprs-archives.copernicus.org/articles/XLVIII-5-W3-2025/17/2025/isprs-archives-XLVIII-5-W3-2025-17-2025.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://isprs-archives.copernicus.org/articles/XLVIII-5-W3-2025/17/2025/isprs-archives-XLVIII-5-W3-2025-17-2025.pdfDirect OA link when available
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Point cloud, Photogrammetry, Artificial intelligence, Random forest, Computer science, Support vector machine, Machine learning, Segmentation, Structure from motion, Boosting (machine learning), Image segmentation, Computer vision, Aerial image, Cultural heritage, Stereo cameras, Machine vision, Documentation, Rendering (computer graphics), Algorithm, Lidar, Gradient boosting, Point (geometry), Aerial survey, Stereopsis, Statistical classification, Decision treeTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| primary_location.pdf_url | https://isprs-archives.copernicus.org/articles/XLVIII-5-W3-2025/17/2025/isprs-archives-XLVIII-5-W3-2025-17-2025.pdf |
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| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
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| primary_location.raw_source_name | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| primary_location.landing_page_url | https://doi.org/10.5194/isprs-archives-xlviii-5-w3-2025-17-2025 |
| publication_date | 2025-11-12 |
| publication_year | 2025 |
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