Abstract
Quantity and distribution of Ground Control Points (GCPs) play a significant role in determining the positional accuracy of UAV photogrammetry. A dense GCP network helps in achieving good accuracy. However, the cost, time, and feasibility of setting up a dense network are challenging. Therefore, it is crucial to assess whether high accuracy can be achieved using minimal GCPs and its optimal distribution. This study investigated the effects of quantity, quality, horizontal, and vertical distribution using 0, 3–11 GCPs to identify a suitable configuration for a sparse GCP network. Thirty-eight configurations were experimented by distributing GCPs in the corners, edges, centre and vertically. Also, another sixteen configurations were used to understand the influence of incorrectly surveyed GCPs on positional accuracy. Horizontal and vertical Root Mean Square Error (RMSE) values were calculated from 79 Check Points for accuracy assessment. Initially, on assessing the effect of quantity, a higher count of GCPs produced high accuracy, but specific configurations using 4–5 GCPs rendered accuracy levels similar to 9–11 GCPs. On further investigation, configurations with few GCPs at the corners showed better accuracy than GCPs distributed only in the edge or centre. A significant reduction in RMSEz of ± 1.5 cm was witnessed by adding vertically distributed GCPs. Based on the results, configurations using 4–5 GCPs distributed vertically and at corners equalled the RMSE values of configurations using 8–11 GCPs, proving it to be an ideal distribution while using fewer GCPs. The poor quality of GCP resulted in low positional accuracy when a sparse number of GCPs were used.
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The data and codes that support the findings of this study are available with the identifier(s) at the private link https://figshare.com/s/f3b5c1a1276e2df5fff9
References
Agüera-Vega F, Carvajal-Ramírez F, Martínez-Carricondo P (2017) Assessment of photogrammetric mapping accuracy based on variation ground control points number using unmanned aerial vehicle. Measur 98:221–227. https://doi.org/10.1016/j.measurement.2016.12.002
Barba S, Barbarella M, Di Benedetto A, Fiani M, Gujski L, Limongiello M (2019) Accuracy Assessment of 3D Photogrammetric Models from an Unmanned Aerial Vehicle. Drones 3:79. https://doi.org/10.3390/drones3040079
Bolkas D (2019) Assessment of GCP Number and Separation Distance for Small UAS Surveys with and without GNSS-PPK Positioning. J Surv Eng 145:04019007. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000283
DJI GO 4 - Download Center - DJI [WWW Document], n.d. URL https://www.dji.com/downloads/djiapp/dji-go-4 (accessed 12.31.22).
Elkhrachy I (2021) Accuracy Assessment of Low-Cost Unmanned Aerial Vehicle (UAV) Photogrammetry. Alex Eng J 60:5579–5590. https://doi.org/10.1016/j.aej.2021.04.011
Eltner, A, Sofia, G (2020) Structure from motion photogrammetric technique, in: Developments in Earth Surface Processes. Elsevier, pp. 1–24. https://doi.org/10.1016/B978-0-444-64177-9.00001-1
Forlani G, Dall’Asta E, Diotri F, Cella UM, di, Roncella R, Santise M (2018) Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning. Remote Sens 10(2):311. https://doi.org/10.3390/rs10020311
Furukawa, Y, Ponce, J (2007) Accurate, Dense, and Robust Multi-View Stereopsis, in: 2007 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1–8. https://doi.org/10.1109/CVPR.2007.383246
Gerke M, Przybilla H-J (2016) Accuracy Analysis of Photogrammetric UAV Image Blocks: Influence of Onboard RTK-GNSS and Cross Flight Patterns. pfg 1:17–30. https://doi.org/10.1127/pfg/2016/0284
Gindraux S, Boesch R, Farinotti D (2017) Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles’ Imagery on Glaciers. Remote Sens 9:186. https://doi.org/10.3390/rs9020186
Guan S, Zhu Z, Wang G (2022) A Review on UAV-Based Remote Sensing Technologies for Construction and Civil Applications. Drones 6:117. https://doi.org/10.3390/drones6050117
Hugenholtz C, Brown O, Walker J, Barchyn T, Nesbit P, Kucharczyk M, Myshak S (2016) Spatial Accuracy of UAV-Derived Orthoimagery and Topography: Comparing Photogrammetric Models Processed with Direct Georeferencing and Ground Control Points. Geomatica 70:21–30. https://doi.org/10.5623/cig2016-102
James MR, Robson S, d’Oleire-Oltmanns S, Niethammer U (2017) Optimising UAV topographic surveys processed with structure-from-motion: Ground control quality, quantity and bundle adjustment. Geomorphol 280:51–66. https://doi.org/10.1016/j.geomorph.2016.11.021
Liu X, Lian X, Yang W, Wang F, Han Y, Zhang Y (2022) Accuracy Assessment of a UAV Direct Georeferencing Method and Impact of the Configuration of Ground Control Points. Drones 6:30. https://doi.org/10.3390/drones6020030
Manfreda S, Dvorak P, Mullerova J, Herban S, Vuono P, ArranzJustel J, Perks M (2019) Assessing the Accuracy of Digital Surface Models Derived from Optical Imagery Acquired with Unmanned Aerial Systems. Drones 3:15. https://doi.org/10.3390/drones3010015
Marr D, Poggio TA (1976) From understanding computation to understanding neural circuitry. Neurosci Res Program Bull 15:470–488
Martínez-Carricondo P, Agüera-Vega F, Carvajal-Ramírez F, Mesas-Carrascosa F-J, García-Ferrer A, Pérez-Porras F-J (2018) Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points. Int J Appl Earth Obs Geoinf 72:1–10. https://doi.org/10.1016/j.jag.2018.05.015
McMahon C, Mora OE, Starek MJ (2021) Evaluating the Performance of sUAS Photogrammetry with PPK Positioning for Infrastructure Mapping. Drones 5:50. https://doi.org/10.3390/drones5020050
Nex F, Remondino F (2014) UAV for 3D mapping applications: a review. Appl Geomatics 6:1–15. https://doi.org/10.1007/s12518-013-0120-x
Oniga, V-E, Breaban, A-I, Statescu, F (2018) Determining the Optimum Number of Ground Control Points for Obtaining High Precision Results Based on UAS Images, in: The 2nd International Electronic Conference on Remote Sensing. Presented at the International Electronic Conference on Remote Sensing, MDPI, p. 352. https://doi.org/10.3390/ecrs-2-05165
Park S, Choi Y (2020) Applications of Unmanned Aerial Vehicles in Mining from Exploration to Reclamation: A Review. Minerals 10:663. https://doi.org/10.3390/min10080663
Park JW, Yeom DJ (2022) Method for establishing ground control points to realize UAV-based precision digital maps of earthwork sites. J Asian Architect Build Eng 21:110–119. https://doi.org/10.1080/13467581.2020.1869023
PIX4Dcapture: Free drone flight planning mobile app | Pix4D [WWW Document], n.d. URL https://www.pix4d.com/product/pix4dcapture (accessed 12.31.22)
PIX4Dmapper: Professional photogrammetry software for drone mapping | Pix4D [WWW Document], n.d. URL https://www.pix4d.com/product/pix4dmapper-photogrammetry-software (accessed 12.31.22)
Room MHM, Ahmad A, Rosly MA (2019) Assessment of different unmanned aerial vehicle system for production of photogrammerty products. Int Arch Photogramm Remote Sens Spatial Inf Sci XLII-4/W16:549–554. https://doi.org/10.5194/isprs-archives-XLII-4-W16-549-2019
Ruzgienė B, Berteška T, Gečyte S, Jakubauskienė E, Aksamitauskas VČ (2015) The surface modelling based on UAV Photogrammetry and qualitative estimation. Measur 73:619–627. https://doi.org/10.1016/j.measurement.2015.04.018
Seitz, SM, Curless, B, Diebel, J, Scharstein, D, Szeliski, R (2006) A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, in: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06). pp. 519–528. https://doi.org/10.1109/CVPR.2006.19
Sibanda M, Mutanga O, Chimonyo VGP, Clulow AD, Shoko C, Mazvimavi D, Dube T, Mabhaudhi T (2021) Application of Drone Technologies in Surface Water Resources Monitoring and Assessment: A Systematic Review of Progress, Challenges, and Opportunities in the Global South. Drones 5:84. https://doi.org/10.3390/drones5030084
Sivakumar, M, Tyj, NM (2021) A Literature Survey of Unmanned Aerial Vehicle Usage for Civil Applications. https://doi.org/10.1590/jatm.v13.1233
Tahar KN (2013) An evaluation on different number of ground control points in unmanned aerial vehicle photogrammetric block. Int Arch Photogramm Remote Sens Spatial Inf Sci XL-2/W2:93–98. https://doi.org/10.5194/isprsarchives-XL-2-W2-93-2013
Tomaštík J, Mokroš M, Saloň Š, Chudý F, Tunák D (2017) Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy. Forests 8:151. https://doi.org/10.3390/f8050151
Tonkin T, Midgley N (2016) Ground-Control Networks for Image Based Surface Reconstruction: An Investigation of Optimum Survey Designs Using UAV Derived Imagery and Structure-from-Motion Photogrammetry. Remote Sens 8:786. https://doi.org/10.3390/rs8090786
Ullman S (1979) The interpretation of structure from motion. Proc R Soc Lond B Biol Sci 203:405–426. https://doi.org/10.1098/rspb.1979.0006
Ulvi A (2021) The effect of the distribution and numbers of ground control points on the precision of producing orthophoto maps with an unmanned aerial vehicle. J Asian Architect Build Eng 20:806–817. https://doi.org/10.1080/13467581.2021.1973479
Villanueva JKS, Blanco AC (2019) Optimization of ground control point (gcp) configuration for unmanned aerial vehicle (uav) survey using structure from motion (SFM). Int Arch Photogramm Remote Sens Spatial Inf Sci XLII-4/W12:167–174. https://doi.org/10.5194/isprs-archives-XLII-4-W12-167-2019
Westoby MJ, Brasington J, Glasser NF, Hambrey MJ, Reynolds JM (2012) ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphol 179:300–314. https://doi.org/10.1016/j.geomorph.2012.08.021
Yu JJ, Kim DW, Lee EJ, Son SW (2020) Determining the Optimal Number of Ground Control Points for Varying Study Sites through Accuracy Evaluation of Unmanned Aerial System-Based 3D Point Clouds and Digital Surface Models. Drones 4:49. https://doi.org/10.3390/drones4030049
Yun B-Y, Sung S-M (2018) Location Accuracy of Unmanned Aerial Photogrammetry Results According to Change of Number of Ground Control Points. J Korean Assoc Geographic Inf Stud 21:24–33. https://doi.org/10.11108/KAGIS.2018.21.2.024
Acknowledgements
The authors would like to thank the Post Graduate students of the Department of Geography, University of Madras, batch 2021-2023, for their assistance during the field survey. We would also like to sincerely thank the Department of Survey and Settlement, Government of Tamil Nadu, for their support during the field survey.
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This work was supported by the Department of Science and Technology (DST)-National Resources Data Management System (NRDMS), Government of India under Grant No 62/11 & 63/11.
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D S, D.S., N, M., S, S. et al. Influence of quantity, quality, horizontal and vertical distribution of ground control points on the positional accuracy of UAV survey. Appl Geomat 15, 897–917 (2023). https://doi.org/10.1007/s12518-023-00531-w
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DOI: https://doi.org/10.1007/s12518-023-00531-w