The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments
Open accessAbstract
The availability of high-resolution Digital Surface Models of coastal environments is of increasing interest for scientists involved in the study of the coastal system processes. Among the range of terrestrial and aerial methods available to produce such a dataset, this study tests the utility of the Structure from Motion (SfM) approach to low-altitude aerial imageries collected by Unmanned Aerial Vehicle (UAV). The SfM image-based approach was selected whilst searching for a rapid, inexpensive, and highly automated method, able to produce 3D information from unstructured aerial images. In particular, it was used to generate a dense point cloud and successively a high-resolution Digital Surface Models (DSM) of a beach dune system in Marina di Ravenna (Italy). The quality of the elevation dataset produced by the UAV-SfM was initially evaluated by comparison with point cloud generated by a Terrestrial Laser Scanning (TLS) surveys. Such a comparison served to highlight an average difference in the vertical values of 0.05 m (RMS = 0.19 m). However, although the points cloud comparison is the best approach to investigate the absolute or relative correspondence between UAV and TLS methods, the assessment of geomorphic features is usually based on multi-temporal surfaces analysis, where an interpolation process is required. DSMs were therefore generated from UAV and TLS points clouds and vertical absolute accuracies assessed by comparison with a Global Navigation Satellite System (GNSS) survey. The vertical comparison of UAV and TLS DSMs with respect to GNSS measurements pointed out an average distance at cm-level (RMS = 0.011 m). The successive point by point direct comparison between UAV and TLS elevations show a very small average distance, 0.015 m, with RMS = 0.220 m. Larger values are encountered in areas where sudden changes in topography are present. The UAV-based approach was demonstrated to be a straightforward one and accuracy of the vertical dataset was comparable with results obtained by TLS technology.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Computer vision: algorithms and applications
(2011) · Choice Reviews Online · doi:10.5860/choice.48-5140 · Cited by 3,911
Shape and motion from image streams under orthography: a factorization method
Tomasi, C., & Kanade, T. (1992) · International Journal of Computer Vision · doi:10.1007/bf00129684 · Cited by 2,878
Photo tourism: exploring photo collections in 3D
Snavely, N., Seitz, S. M., & Szeliski, R. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141964 · Cited by 2,866
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) · doi:10.1109/cvpr.2006.19 · Cited by 2,457
Computer Vision: Algorithms and Applications
Szeliski, R. (2011) · Texts in Computer Science · doi:10.1007/978-1-84882-935-0 · Cited by 1,365
Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets
Wheaton, J. M., Brasington, J., Darby, S. E., & Sear, D. A. (2010) · Earth Surface Processes and Landforms · doi:10.1002/esp.1886 · Cited by 1,160
Topographic structure from motion: a new development in photogrammetric measurement
Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L., & Carbonneau, P. E. (2013) · Earth Surface Processes and Landforms · doi:10.1002/esp.3366 · Cited by 1,126
The interpretation of structure from motion
Ullman, S. (1979) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1979.0006 · Cited by 1,044
Turner, D., Lucieer, A., & Watson, C. (2012) · Remote Sensing · doi:10.3390/rs4051392 · Cited by 744
Development of a UAV-LiDAR System with Application to Forest Inventory
Wallace, L., Lucieer, A., Watson, C., & Turner, D. (2012) · Remote Sensing · doi:10.3390/rs4061519 · Cited by 710
Cited by
Structure from motion photogrammetry in physical geography
Smith, M. W., Carrivick, J. L., & Quincey, D. J. (2016) · Progress in Physical Geography: Earth and Environment · doi:10.1177/0309133315615805 · Cited by 772
Turner, I. L., Harley, M. D., & Drummond, C. D. (2016) · Coastal Engineering · doi:10.1016/j.coastaleng.2016.03.011 · Cited by 455
Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?
Chabot, D., & Bird, D. M. (2015) · Journal of Unmanned Vehicle Systems · doi:10.1139/juvs-2015-0021 · Cited by 286
Metadata
- Authors
- Mancini, F., Dubbini, M., Gattelli, M., Stecchi, F., Fabbri, S., & Gabbianelli, G.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2013-12-09
- Volume
- 5
- Issue
- 12
- Pages
- 6880-6898
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W1965711210
- Cited by
- 770
- References
- 26
- FWCI
- 55.00
- Type
- journal article
- Language
- en
- Open access
- gold
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: Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments. https://app.cochat.ai/papers/10.3390/rs5126880
- The paper
- Mancini, F., Dubbini, M., Gattelli, M., Stecchi, F., Fabbri, S., & Gabbianelli, G. (2013). Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments. Remote Sensing. https://doi.org/10.3390/rs5126880
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.