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The main goal of the work was an attempt to compare the free and commercial photogrammetry software for processing the pictures taken with a camera drone, a traditional digital camera and a smartphone. Due to a wide range of programs on the market, four were selected for comparison (Agisoft Metashape, DroneDeploy, VisualSfM, COLMAP). Their brief description was presented, and then the photos were processed in each of them. Three sets of photographs were used for the processing (part of a residential area, photos of a building, and photos of a tree trunk). As a result, the capabilities of the selected applications were presented on the basis of various input data. Not every program was able to deliver all the desired products. Moreover, they differ depending on the software. The commercial applications have more functionalities. On the other hand, the open-source solutions allow for the development of algorithms. Working in any environment had its own characteristics. The selected applications were compared on the basis of the processing and the results obtained. Due to many aspects of their evaluation, it turned out that the research topic was very extensive. Moreover, it was found that it is very difficult to make an objective statement of the tested programs, because the same program can be scored differently, depending on the user’s needs, capabilities and knowledge.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
213--225
Opis fizyczny
Bibliogr. 37 poz., rys., tab.
Twórcy
autor
- Department of Environmental Engineering and Geodesy, University of Life Sciences in Lublin, ul. Leszczyńskiego 7, 20-069 Lublin, Poland
autor
- Department of Environmental Engineering and Geodesy, University of Life Sciences in Lublin, ul. Leszczyńskiego 7, 20-069 Lublin, Poland
autor
- Department of Geodesy, University of Warmia and Mazury in Olsztyn, ul. Michała Oczapowskiego 2, 10-719 Olsztyn, Poland
Bibliografia
- 1. Abed Al Aziz, Ayat; Ayaydeh, Majdoleen; Abu Attalah, Mariam. 2015. The use of mobile phone camera in close range photogrammetry. College of Engineering, Civil & Architectural Engineering Department, Surveying and Geomatics Engineering, Graduation Project, 18–32.
- 2. Adamski M., Urbaniak W., Dąbrowska A., Dąbrowski A. 2018. Testing of unmanned aerial vehicles for monitoring of environmental pollution. Przegląd Elektrotechniczny, 9, 16 (in Polish). doi:10.15199/48.2018.09.03.
- 3. Agisoft. Available: https://www.agisoft.com/ (Accessed: 23.10.2020).
- 4. Aicardi I., Lingua A., Piras M. 2014. Evaluation of mass market devices for the documentation of the cultural heritage. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-5, 2–7. doi: 10.5194/isprsarchives-XL-5–17–2014.
- 5. Bemis S.P, S. Micklethwaite, Tuner D., James M. R., Akciz S. O., Thiele S., Bangash H. L. 2014. Ground-based and UAV-Based photogrammetry: A multi-scale, high-resolution mapping tool for Structural Geology and Paleoseismology. Journal of Structural Geology, 69, 172–177. doi: 10.1016/j.jsg.2014.10.007.
- 6. Błaszczak-Bąk W., Janowski A., Srokosz P. 2018. High performance filtering for big datasets from Airborne Laser Scanning with CUDA technology. Survey Review, 50, 360, 262–269, doi: 10.1080/00396265.2016.1264180.
- 7. Corrigan F. 2020. 12 Best Photogrammetry Software For 3D Mapping Using Drones. https://www.dronezon.com/learn-about-drones-quadcopters/drone-3d-mapping-photogrammetry-software-forsurvey-gis-models/ (Accessed: 20.10.2020).
- 8. Dabove P., Grasso N. Piras M. 2019. SmartphoneBased Photogrammetry for the 3D Modeling of a Geomorphological Structure. Applied Sciences, 9, 3884, 2–18. doi: 10.3390/app9183884.
- 9. DJI. 2017. DJI Inspire 1 User Manual. DJI. 12, 2.2, 37–40.
- 10. DroneDeploy. Available: https://www.dronedeploy.com/ (Accessed: 23.10.2020).
- 11. Rahaman H., Champion E. 2019. To 3D or Not 3D: Choosing a Photogrammetry Workflow for Cultural Heritage Groups. School of Media, Creative Arts, and Social Inquiry, Curtin University, Perth. Heritage 2019, 2, 112, 1836. doi: 10.3390/heritage2030112.
- 12. http://ccwu.me/vsfm/ (Accessed: 23.10.2020).
- 13. https://www.agisoft.com/downloads/sample-data/. (Accessed: 15.10.2020).
- 14. Jiang S., Jiang C., Jiang W. 2020. Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM tools. International Society for Photogrammetry and Remote Sensing, 167, 230–251. doi: 10.1016/j.isprsjprs.2020.04.016.
- 15. Khalil O. Al., Grussenmeyer P. 2019. 2D & 3D reconstruction workflows from archive images, case study of damaged monuments in Bosra Al-sham city (Syria). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W15, 55–61. doi: 10.5194/isprs-archives-XLII-2-W15–55–2019.
- 16. Królikowski J. 2017. An overview of the software for processing unmanned photogrammetric images. How to process data from a drone? Niezbędnik Miesięcznika Geodeta. Drony dla Geodety, 1(265), 16–18 (in Polish).
- 17. Królikowski J. 2018. A list of software for processing drones’ images. Like a string. Niezbędnik Miesięcznika Geodeta. Drony dla Geodety, 1(2), 48–50 (in Polish).
- 18. Kurczyński Z. 2014. Fotogrametria. Wydawnictwo Naukowe PWN S.A., Warszawa, 122–125, 345–346.
- 19. Mikita T., Jantara P., Surový P. 2016. Forest Stand Inventory Based on Combined Aerial and Terrestrial Close-Range Photogrammetry. Forests, 7, 165, 4–5. doi: 10.3390/f7080165.
- 20. Mohan M., Silva C.A., Klauberg C., Jat P., Catts G., Cardil A., Hudak A. T., Dia M. 2017. Individual Tree Detection from Unmanned AerialVehicle (UAV) Derived Canopy Height Model inan Open Canopy Mixed Conifer Forest. Forests, 8(9), 340, 2–12. doi: 10.3390/f8090340.
- 21. Morgan J.A, Brogan D.J. 2016. How to VisualSFM. Department of Civil & Environmental Engineering Colorado State University Fort Collins, 1–21.
- 22. Niederheiser R., Mokroš M., Lange J., Petschko H., Prasicek G., Elberink S. O. 2016. Deriving 3D point clouds from terrestrial photographs – comparison of different sensors and software. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B5, 685–692. doi:10.5194/isprsarchives-XLI-B5–685–2016.
- 23. Niethammer U., Rothmund S., Schwaderer U., Zeman J., Joswig M. 2011. Open source imageprocessing tools for low-cost UAV-based landslide investigations. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-1/C22, 2–4. doi: 10.5194/isprsarchives-XXXVIII-1-C22–161–2011.
- 24. Ourloglou O., Stefanidis K., Dimitriou E. 2020. Assessing Nature-Based and Classical Engineering Solutions for Flood-Risk Reduction in Urban Streams. Journal of Ecological Engineering. 21, 2, 46–52. doi: 10.12911/22998993/116349.
- 25. Plichta A., Wyczałek M., Wyczałek I. 2017. Graphical part of land and buildings registry based on aerial photos from the board of unmanned aerial vehicle (UAV). Zeszyty Naukowe Uniwersytetu Zielonogórskiego. Inżynieria Środowiska, 165, 45, 42–46 (in Polish).
- 26. Preuss R. 2014. Automation of Image Data Processing. Archiwum Fotogrametrii, Kartografii i Teledetekcji, 26, 119–127. doi: 10.14681/afkit.2014.010 (in Polish).
- 27. Prisacariu V. 2017. The history and the evolution of UAVs from the beginning till the 70s. Journal of Defense Resources Management, 8/1(14), 1–7.
- 28. Remondino F, Del Pizzo S, Kersten T, Troisi S. 2012. Low-cost and open-source solutions for automated image orientation–a critical overview. Progress in Culturale Heritage Preservation. Lecture Notes in Computer Science, 7616, 40–54. doi: 10.1007/978–3-642–34234–9.
- 29. Ren H., Zhao Y., Xiao W., Hu Z. 2019. A review of UAV monitoring in mining areas: current status and future perspectives. International Journal of Coal Science & Technology, 6(3), 320–333, doi: 10.1007/s40789–019–00264–5.
- 30. Sarhan M.S. 2011. Feasibility study of using mobile phone camera in digital close range photogrammetry. Journal of Thi-Qar University, 1, 7, 61–66.
- 31. Schoenberger J. L. 2020. Available: https://colmap.github.io/index.html (Accessed: 23.10.2020).
- 32. Sona G., Pinto L, Pagliari D., Gini R. 2014. Experimental analysis of different software packages for orientation and digital surface modelling from UAV images. Earth Science Informatics, 7, 97–107. doi: 10.1007/s12145–013–0142–2.
- 33. Uebel M. 2020. 2020 Best Photogrammetry Software (Some are Free). https://all3dp.com/1/bestphotogrammetry-software/ (Accessed: 20.10.2020).
- 34. Vacca G., Furfaro G., Dessì A. 2018. The use of the UAV images for the building 3D model generation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W8, 217–223. doi: 10.5194/isprs-archives-XLII-4-W8–217–2018.
- 35. Villa, T.F., Gonzalez, F., Miljievic, B., Ristovski, Z.D., Morawska, L. 2016. An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives. Sensors, 16, 1071–1072. doi: 10.3390/s16071072.
- 36. Wilfried L. 2009. Digital Photogrammetry. A Practical Course. Springer Berlin Heidelberg, Dusseldorf, 11.
- 37. Zou Y., Barati M., Rey Castillo E., Amor R. 2019. Automated UAV route planning for bridge inspection using BIM-GIS data. Conference: 4th International Conference on Civil and Building Engineering Informatics At: Sendai, Japan, 384–391.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3db31043-cc70-47dc-b0df-79fc3caada14