Cost-Aware UAV Photogrammetric Mission Design: Experimental Trade-Offs Between Overlap, Geometry, and Mapping Quality

Abstract

Unmanned Aerial Vehicle (UAV) photogrammetry enables high-resolution mapping and 3D reconstruction, yet operational and processing costs often scale rapidly with conservative mission designs (e.g., high overlap and redundant geometries). This paper presents an experimentally validated, cost-aware network-design study that quantifies cost-quality trade-offs in urban UAV photogrammetry. Five mission strategies—reduced sidelap with increased endlap, cross-flight compensation, partial high-overlap calibration, multi-altitude acquisition, and oblique cross-flight integration—are evaluated using a controlled experimental campaign over two urban test areas (2 × 20 ha), comprising 98 test blocks with overlaps ranging from 60% to 95%, sidelap from 20% to 80%, image counts from 70 to 2961, 7 check points, 15-17 ground control points, and GSD values between 2.6 cm and 4.6 cm, including nadir, oblique, cross-flight, and multi-altitude imagery. Each configuration is assessed using three indicators: (i) cost (flight and processing cost proxies), (ii) completeness, quantified by the number of reconstructed tie points, and (iii) accuracy, defined as a combined image-ground error at check points. Results show that cost reductions of over 50% in both flight and processing proxies can be achieved under the tested conditions while maintaining checkpoint accuracy comparable to a high-overlap reference configuration, provided that reduced overlap is compensated by stronger network geometry (e.g., cross-flight and/or oblique views). The analysis highlights product-dependent recommendations: vector map (MAP) generation can remain reliable even with very low sidelap (down to approximately 20%) when supported by adequate longitudinal overlap, whereas ortho-image mosaic (OIM) production requires at least moderate overlap in both directions (typically ≥60% endlap and sidelap) to ensure radiometric and geometric consistency. In contrast, dense 3D mesh reconstruction demands substantially stronger network geometry, including cross-flight and oblique imagery in addition to nadir views, with overlap levels exceeding 60% and preferably approaching 80%. These findings provide practical mission-planning guidelines that support efficient autonomous and semi-autonomous UAV mapping workflows.

Keywords

UAV photogrammetry / Cost-aware mission planning / Photogrammetric network design / Image overlap strategy / Operational and processing cost / Tie-point completeness / Accuracy assessment / Oblique and cross-flight imagery / Multi-altitude acquisition / Autonomous UAV mapping

Supplementary Materials

The following supporting information can be found at: https://www.sciepublish.com/article/pii/901, Supplementary Material (Excel): Cost/Quality Computation Sheet for all 98 configurations.

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this manuscript, the author(s) used ChatGPT in order to assist with language editing and improving the clarity of the text. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

Acknowledgments

We would like to acknowledge the support of Mohammad Sadegh Rahiminia in UAV data collection.

Author Contributions

Conceptualization, M.S.; Methodology, M.S.; Software, O.F.; Validation, M.S., A.A. and H.A.; Investigation, A.A. and M.S.; Resources, O.F.; Data Curation, O.F.; Writing—Original Draft Preparation, M.S.; Writing—H.A., M.S. and A.A.; Project Administration, M.S.

Ethics Statement

The study was conducted according to the guidelines of the Declaration of Helsinki.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data that support the findings of this study, including processed datasets, mission configurations, and derived metrics, are available from the corresponding author upon reasonable request. Summary tables and analysis outputs are provided in supplementary material to facilitate transparency and reproducibility. (Supplementary Material (Excel): Cost/Quality Computation Sheet for all 98 configurations).

Funding

This research received no external funding.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] Colomina I, Molina P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2014, 92, 79-97. DOI:10.1016/j.isprsjprs.2014.02.013

[2] Haala N, Cramer M, Weimer F, Trittler M. Performance test on UAV-based photogrammetric data collection. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2012, 38, 7-12. DOI:10.5194/isprsarchives-XXXVIII-1-C22-7-2011

[3] Maboudi M, Homaei M, Song S, Malihi S, Saadatseresht M, Gerke M. A review on viewpoints and path planning for UAV-based 3-D reconstruction. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2023, 16, 5026-5048. DOI:10.1109/JSTARS.2023.3276427

[4] Song J, Zhao K, Liu Y. Survey on mission planning of multiple unmanned aerial vehicles. Aerospace 2023, 10, 208. DOI:10.3390/aerospace10030208

[5] Chaudhry MH, Ahmad A, Gulzar Q. Impact of UAV surveying parameters on mixed urban landuse surface modelling. ISPRS Int. J. Geo-Inf. 2020, 9, 656. DOI:10.3390/ijgi9110656

[6] Aicardi I, Chiabrando F, Grasso N, Lingua AM, Noardo F, Spanò A. UAV photogrammetry with oblique images: First analysis on data acquisition and processing. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2016, 41, 835-842. DOI:10.5194/isprs-archives-XLI-B1-835-2016

[7] Chiabrando F, Lingua A, Maschio P, Teppati Losè L. The influence of flight planning and camera orientation in UAVs photogrammetry: A test in the area of Rocca San Silvestro (LI), Tuscany. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2017, 42, 163-170. DOI:10.5194/isprs-archives-XLII-2-W3-163-2017

[8] Zhang S, Zhang W, Liu C. Model-Based Multi-UAV Path Planning for High-Quality 3D Reconstruction of Buildings. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2023, XLVIII-1/W2, 1923-1928. DOI:10.5194/isprs-archives-XLVIII-1-W2-2023-1923-2023

[9] Khujamatov EH, Abdullaev M, Umirzakova S. Analytical modeling of hybrid CNN-Transformer dynamics for emotion classification. Mathematics 2025, 14, 85. DOI:10.3390/math14010085

[10] Baarda W. A Testing Procedure for Use in Geodetic Networks; Netherlands Geodetic Commission: Apeldoorn, The Netherlands, 1968.

[11] Teunissen PJG. Testing Theory: An Introduction; Delft University Press: Delft, The Netherlands, 2000.

[12] Triggs B, McLauchlan P, Hartley R, Fitzgibbon A. Bundle Adjustment—A Modern Synthesis. In Vision Algorithms: Theory and Practice; Triggs B, Zisserman A, Szeliski R,Eds.; Springer: Berlin/Heidelberg, Germany, 2000; pp. 298-372. DOI:10.1007/3-540-44480-7_21

[13] Kraus K. Photogrammetry: Geometry from Images and Laser Scans, 2nd ed.; Walter de Gruyter: Berlin, Germany, 2007.

[14] Deb K. Multi-Objective Optimization Using Evolutionary Algorithms; Wiley: Hoboken, NJ, USA, 2001.

[15] Mikhail EM, Bethel JS, McGlone JC. Introduction to Modern Photogrammetry; Wiley: Hoboken, NJ, USA, 2001.

[16] James MR, Robson S, Smith MW. 3-D uncertainty-based topographic change detection with structure-from-motion photogrammetry: Precision maps for ground control and directly georeferenced surveys. Earth Surf. Process. Landf. 2017, 42, 1769-1788. DOI:10.1002/esp.4125

[17] Förstner W, Wrobel B. Photogrammetric Computer Vision; Springer: Cham, Switzerland, 2016. DOI:10.1007/978-3-319-11550-4

[18] Gerke M, Przybilla H-J. Accuracy Analysis of Photogrammetric UAV Image Blocks: Influence of Onboard RTK-GNSS and Cross Flight Patterns. Photogramm. -Fernerkund. -Geoinf. 2016, 1, 17-30. DOI:10.1127/pfg/2016/0284

[19] Remondino F, Fraser CS. Digital camera calibration methods: Considerations and comparisons. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2006, 36, 266-272. DOI:10.3929/ethz-b-000158067

[20] Remondino F, Spera MG, Nocerino E, Menna F, Nex F. State of the art in high density image matching. Photogramm. Rec. 2014, 29, 144-166. DOI:10.1111/phor.12063

[21] Galceran E, Carreras M. A survey on coverage path planning for robotics. Robot. Auton. Syst. 2013, 61, 1258-1276. DOI:10.1016/j.robot.2013.09.004

[22] Roncella R, Forlani G. UAV Block Geometry Design and Camera Calibration: A Simulation Study. Sensors 2021, 21, 6090. DOI:10.3390/s21186090

[23] Fraser CS. Network design considerations for non-topographic photogrammetry. Photogramm. Eng. Remote Sens. 1984, 50, 1115-1126. Available online: https://www.researchgate.net/publication/243702234_Network_design_considerations_for_non-topographic_photogrammetry (accessed on 1 December 2025).