Implementation of Unmanned Aerial Vehicles in the Automated Assessment of Geodetic Database Validity

• Autorzy: Maciąg Klaudia , Maciąg Michał , Leń Przemysław

Identyfikatory

DOI

10.12913/22998624/192264

• Języki publikacji: EN

Abstrakty

EN

Nowadays, the progress of technology covers, among other things, the development of modern techniques and high technologies used in land surveys. Unmanned aerial vehicles (UAVs), as a good alternative to conventional land survey techniques, have currently played an increasing role. The advantages of using unmanned aerial vehicles in photogrammetric measurements include a relatively short mission time for large-area surveys. In addition, photogrammetric products have a wider range of applications compared with conventional geodetic surveys. Many scientific publications delve into the quality of photogrammetric products, but the accuracy of UAVs in the context of geodetic standards has not been investigated in full. In this paper, we attempt to fill the observed research gap. Our research has analysed the position of objects recorded in geodetic databases referring to their counterparts based on an accurate orthophotomap from a photogrammetry campaign employing an unmanned aerial vehicle. The outcomes were referenced with land survey accuracy standards set out by relevant legislation. To ensure a smooth assessment of the result's accuracy we designed a computing algorithm with a module for selecting comparable points and verifying the results. The tool can be implemented in surveys carried out in any area thanks to open-source GIS software. Our analysis showed that a detailed orthophotomap delivered using UAVs can be a valuable data source on objects recorded in geodetic databases covering selected cadastral and topographic objects and land development components. A general verification of the accuracy and validity of a geodetic numerical map and preliminary detection of areas for potential updates can be a particularly useful application of photogrammetry.

Słowa kluczowe

EN

UAV; orthophotomap; base map; algorithm; accuracy

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no. 7
• Strony: 379--395
• Opis fizyczny: Bibliogr. 66 poz., fig., tab.

Twórcy

autor

Maciąg Klaudia

• Faculty of Environmental Engineering and Geodesy, University of Life Sciences in Lublin, ul. Akademicka 13, 20-950 Lublin, Poland

• https://orcid.org/0000-0003-4099-9886

autor

Maciąg Michał

• Faculty of Environmental Engineering and Geodesy, University of Life Sciences in Lublin, ul. Akademicka 13, 20-950 Lublin, Poland

• https://orcid.org/0000-0002-8745-0009

autor

Leń Przemysław

• Faculty of Geodesy and Geotechnics, Rzeszów University of Technology, al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland

• https://orcid.org/0000-0003-0810-9986

Bibliografia

• 1. Ćwiąkała P., Gruszczyński W., Stoch T., Puniach E., Mrocheń D., Matwij W., Matwij K., Nędzka M., Sopata P., Wójcik A. UAV Applications for determination of land deformations caused by underground mining. Remote Sens. 2020, 12, 1733. https://doi.org/10.3390/rs12111733
• 2. Borowik G., Kożdoń-Dębecka M., Strzelecki S. Mutable Observation Used by Television Drone Pilots: Efficiency of Aerial Filming Regarding the Quality of Completed Shots. Electron. 2022, 11, 3881. https://doi.org/10.3390/electronics11233881
• 3. Kovanič Ľ., Topitzer B., Peťovský P., Blišťan P., Gergeľová M.B., Blišťanová M. Review of Photogrammetric and Lidar Applications of UAV. Appl. Sci. 2023, 13, 6732. https://doi.org/10.3390/app13116732
• 4. Sestras P., Bilașco Ș., Roșca S., Dudic B., Hysa A., Spalević V. Geodetic and UAV monitoring in the sustainable management of shallow landslides and erosion of a susceptible urban environment. Remote Sens. 2021, 3, 385. https://doi.org/10.3390/rs13030385
• 5. 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, 14, 17–30. https://doi.org/10.1127/pfg/2016/0284
• 6. Malihi S., Valadan Zoej M.J., Hahn M. Large-scale accurate reconstruction of buildings employing point clouds generated from UAV imagery. Remote Sens. 2018, 10, 1148. https://doi.org/10.3390/rs10071148
• 7. Marmanis D., Schindler K., Wegner J.D., Galliani S., Datcu M., Stilla U. Classification with an edge: Improving semantic image segmentation with boundary detection. ISPRS J. of Photogramm. and Remote Sens. 2018, 135, 158–172 https://doi.org/10.1016/j.isprsjprs.2017.11.009
• 8. Ružić I., Benac Č., Jovančević S.D., Radišić M. The Application of UAV for the analysis of geological hazard in Krk Island, Croatia, Mediterranean Sea. Remote Sens. 2021, 13, 1790. https://doi.org/10.3390/rs13091790
• 9. Boccardo P., Chiabrando F., Dutto F., Tonolo F.G., Lingua A. UAV deployment exercise for mapping purposes: evaluation of emergency response applications. Sens. 2015, 15, 15717–15737. https://doi.org/10.3390/s150715717
• 10. Tokarczyk P., Leitao J.P., Rieckermann J., Schindler K., Blumensaat F. High-quality observation of surface imperviousness for urban runoff modelling using UAV imagery. Hydrol. Earth Syst. Sci. 2015, 19, 4215–4228. https://doi.org/10.5194/hess-19-4215-2015
• 11. Skondras A., Karachaliou E., Tavantzis I., Tokas N., Valari E., Skalidi I., Bouvet G.A., Stylianidis E. UAV mapping and 3D modeling as a tool for promotion and management of the urban space. Drones 2022, 6, 115. https://doi.org/10.3390/drones6050115
• 12. Nex F., Remondino F. UAV for 3D mapping applications: A review. Appl. Geomat. 2014, 6, 1–15. https://doi.org/10.1007/s12518-013-0120-x
• 13. Fernandez Galarreta J., Kerle N., Gerke M. UAV- based urban structural damage assessment using object-based image analysis and semantic reasoning. Nat. Hazards Earth Syst. Sci. 2015, 15, 1087–1101. https://doi.org/10.5194/nhess-15-1087-2015
• 14. Vetrivel A., Gerke M., Kerle N., Vosselman G. Segmentation of UAV-based images incorporating 3D point cloud information. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2015, XL-3/W2, 261–268. https://doi.org/10.5194/isprsarchives-XL-3-W2-261-2015
• 15. Vetrivel A., Gerke M., Kerle N., Vosselman G. Identification of structurally damaged areas in airborne oblique images using a visual-Bag-of-Words approach. Remote Sens. 2016, 8(3), 231. https://doi.org/10.3390/rs8030231.
• 16. Hoffmann H., Nieto H., Jensen R., Guzinski R., Zarco-Tejada P., Friborg T. Estimating evaporation with thermal UAV data and two-source energy balance models. Hydrol. Earth Syst. Sci., 2016, 20, 697–713. https://doi.org/10.5194/hess-20-697-2016
• 17. Colomina I., Molina P. Unmanned aerial systems for photogrammetry and remote sensing: A review. IS-PRS J. Photogramm. Remote Sens. 2014, 92, 79–97. https://doi.org/10.1016/j.isprsjprs.2014.02.013
• 18. Mesas-Carrascosa F.J., Notario-García M.D., de Larriva J.E.M., de la Orden M.S., Porras A.G.-F. Validation of measurements of land plot area using UAV imagery. Int. J. Appl. Earth Obs. Geoinform. 2014, 33, 270–279. https://doi.org/10.1016/j.jag.2014.06.009
• 19. Fernández-Hernandez J., González-Aguilera D., Rodríguez-Gonzálvez P., Mancera-Taboada J. Image-based modelling from unmanned aerial vehicle (UAV) photogrammetry: An effective, low-cost tool for archaeological applications. Archaeometry 2015, 57, 128–145. https://doi.org/10.1111/arcm.12078
• 20. Pajares G. Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogramm. Eng. Remote Sens. 2015, 81, 281–330. https://doi.org/10.14358/PERS.81.4.281
• 21. Orihuela A., Molina-Fajardo M.A. UAV photogrammetry surveying for sustainable conservation: The case of Mondújar Castle (Granada, Spain). Sustain. 2021, 13, 24. https://doi.org/10.3390/su13010024
• 22. Du S., Zhang Y., Qin R., Yang Z., Zou Z., Tang Y., Fan C. Building change detection using old aerial images and new LiDAR data. Remote Sens. 2016, 8(12), 1030. https://doi.org/10.3390/rs8121030
• 23. Zhu J.S., Sun K., Jia S., Li Q.Q., Hou X.X., Lin W.D., Liu B.Z., Qiu G.P. Urban traffic density estimation based on ultra-high-resolution UAV video and deep neural network. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2018, 11, 4968–4981. https://doi.org/10.1109/JSTARS.2018.2879368
• 24. Ren H., Zhao Y., Xiao W., Hu Z. A review of UAV monitoring in mining areas: current status and future perspectives. Int. J. Coal. Sci. Technol. 2019, 6, 320–333. https://doi.org/10.1007/s40789-019-00264-5
• 25. Yao H., Qin R., Chen X. Unmanned aerial vehicle for remote sensing applications—A review. Remote Sens. 2019, 11, 1443. https://doi.org/10.3390/rs11121443
• 26. Wu B. Photogrammetry for 3D Mapping in Urban Areas. In: Shi W., Goodchild M.F., Batty M., Kwan M.P., Zhang A. Urban informatics. The Urban Book Series. Springer, Singapore 2021. https://doi.org/10.1007/978-981-15-8983-6_23
• 27. Ahmed F., Mohanta J.C., Keshari A., Yadav P.S. Recent advances in unmanned aerial vehicles: A review. Arab. J. Sci. Eng. 2022, 47, 7963–7984. https://doi.org/10.1007/s13369-022-06738-0.
• 28. Beselly S.M., van der Wegen M., Grueters U., Reyns J., Dijkstra J., Roelvink D. Eleven years of mangrove–mudflat dynamics on the mud volcanoinduced prograding delta in East Java, Indonesia: Integrating UAV and Satellite Imagery. Remote Sens. 2021, 13, 1084. https://doi.org/10.3390/rs13061084
• 29. Sturdivant E.J., Lentz E.E., Thieler E.R., Farris A.S., Weber K.M., Remsen D.P., Miner S., Henderson R.E. UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery. Remote Sens. 2017, 9(10), 1020. https://doi.org/10.3390/rs9101020
• 30. Congress S.S.C., Puppala A.J., Lundberg C.L. Total system error analysis of UAV-CRP technology for monitoring transportation infrastructure assets. Eng. Geol. 2018, 247, 104–116. https://doi.org/10.1016/j.enggeo.2018.11.002
• 31. Guan S., Zhu Z., Wang G.A. Review on UAV-based remote sensing technologies for construction and civil applications. Drones 2022, 6, 117. https://doi.org/10.3390/drones6050117
• 32. Crommelinck S., Bennett R., Gerke M., Nex F., Yang M.Y., Vosselman G. Review of automatic feature extraction from high-resolution optical sensor data for UAV-based cadastral mapping. Remote Sens. 2016, 8, 689. https://doi.org/10.3390/rs8080689
• 33. Alemie B.K., Bennett R.M., Zevenbergen J. Evolving urban cadastres in Ethiopia: The impacts on urban land governance. Land Use Policy 2015, 42, 695–705. https://doi.org/10.1016/j.landusepol.2014.10.001
• 34. Jazayeri I., Rajabifard A., Kalantari M. A geometric and semantic evaluation of 3D data sourcing methods for land and property information. Land Use Policy 2014, 36, 219–230. https://doi.org/10.1016/j.landusepol.2013.08.004
• 35. Turner D., Lucieer A., De Jong S.M. Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV). Remote Sens. 2015, 7(2), 1736–1757. https://doi.org/10.3390/rs70201736
• 36. Wallace L., Lucieer A., Malenovský Z., Turner D., Vopěnka P. Assessment of forest structure using two UAV techniques: A comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests 2016, 7, 62. https://doi.org/10.3390/f7030062
• 37. Ożóg K. The use of unmanned aerial vehicles for the assessment of land boundaries accuracy. J. Water Land Dev. 2019, 45, 94–99. https://doi.org/10.24425/jwld.2020.133050
• 38. Stöcker C., Koeva M.N., Nkerabigwi P., Zevenbergen J.A. UAV Technology: Opportunities to Support the Updating Process of the Rwandan Cadastre. In: FIG Working Week 2020, Amsterdam, Netherlands, 2020. https://www.fig.net/resources/proceedings/fig_proceedings/fig2020/papers/ts07h/TS07H_stocker_koeva_et_al_10290.pdf. (Accessed: 04.12.2023)
• 39. Stöcker C., Nex F., Koeva M., Gerke M. Highquality UAV-based orthophotos for cadastral mapping: guidance for optimal flight configurations. Remote Sens. 2020, 12, 3625. https://doi.org/10.3390/rs12213625
• 40. Stöcker C., Bennett R., Koeva M., Nex F., Zevenbergen J. Scaling up UAVs for land administration: Towards the plateau of productivity. Land Use Policy 2022, 114, 105930, https://doi.org/10.1016/j.landusepol.2021.105930
• 41. Doroż A., Bożek P., Maciąg K., Maciąg M., Leń P. Zastosowanie bezzałogowych statków powietrznych w pozyskiwaniu materiałów fotogrametrycznych na potrzeby realizacji kompleksowych prac scaleniowych [The use of UAV in the acquisition of photogrammetric materials for the implementation of land consolidation]. Prz. Geod. 2023, 6, 26–29. https://doi.org/10.15199/50.2023.06.2 (In Polish)
• 42. Ajayi O.G., Oruma E. On the applicability of integrated UAV photogrammetry and automatic feature extraction for cadastral mapping. Adv. Geod. Geoinf. 2022, 71, 1, e19. https://doi.org/10.24425/gac.2022.141172
• 43. Pyka K. Prawne, techniczne i społeczne aspekty pomiarów fotogrametrycznych [Legal, technical and social aspects of photogrammetric measurements]. Prz. Geod. 2016, 1, 8–12. https://doi.org/10.15199/50.2016.11.1 (In Polish)
• 44. Brach M., Gąsior J. Przydatność niskokosztowego bezzałogowego statku powietrznego do weryfikacji wybranych elementów mapy zasadniczej [Usage of the low cost unmanned aerial vehicle in the master map verification process for selected classes]. Prz. Geod. 2022, 1, 32–35 https://doi.org/10.15199/50.2022.1.4 (In Polish)
• 45. Van Canh L., Xuan Cuong C., Quoc Long N., Thi Thu Ha L., Trung Anh T., Bui, X.-N. Experimental Investigation on the Performance of DJI Phantom 4 RTK in the PPK Mode for 3D Mapping Open-Pit Mines. J. Pol. Miner. Eng. Soc. 2020, 1(2), 65–74. https://doi.org/10.29227/IM-2020-02-10
• 46. Feng Q., Liu J., Gong J. UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis. Remote Sens. 2015, 7, 1074–1094. https://doi.org/10.3390/rs70101074
• 47. Barlow J., Gilham J., Ibarra Cofrã I. Kinematic analysis of sea cliff stability using UAV photogrammetry. Int. J. Remote Sens. 2017, 38, 2464–2479. https://doi.org/10.1080/01431161.2016.1275061
• 48. Borkowski A.S. A Literature review of BIM definitions: Narrow and broad views. Technol. 2023, 11, 176. https://doi.org/10.3390/technologies11060176
• 49. Borkowski A.S. Evolution of BIM: epistemology, genesis and division into periods. J. Inf. Technol. Constr. 2023, 28, 646–661. https://doi.org/10.36680/j.itcon.2023.034
• 50. Borkowski A.S. Experiential learning in the context of BIM[J]. STEM Educ. 2023, 3(3), 190–204. https://doi.org/10.3934/steme.2023012
• 51. Borkowski A.S., Firlińska S.A. Modelling of transmission infrastructure in BIM technology. Prz. Elektrotech. 2022, 98, 12, 133–135. https://doi.org/10.15199/48.2022.12.33
• 52. Borkowski A.S., Kochański Ł., Wyszomirski M. A case study on building information (BIM) and land information (LIM) models including geospatial data. Geomat. Env. Eng. 2023, 17(1) https://doi.org/10.7494/geom.2023.17.1.19
• 53. Borkowski A.S., Łuczkiewicz N. Landscape information model (LIM): a case study of Ołtarzew
• Park in Ożarów Mazowiecki municipality. Bud. Archit. 2023, 22(2), 41–56. https://doi.org/10.35784/bud-arch.3547
• 54. Kurczyński Z., Bakuła K., Karabin M., Kowalczyk M., Markiewicz J.S., Ostrowski W., Podlasia P., Zawieska D. The possibility of using images obtained from the UAS in cadastral works. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2016, XLI-B1, 909–915. https://doi.org/10.5194/isprs-archives-XLI-B1-909-2016
• 55. Karabin M., Bakuła K., Łuczyński R. Verification of the geometrical representation of buildings in cadastre using UAV photogrammetry. Geomat. and Environ. Eng. 2021, 15, 4. https://doi.org/10.7494/geom.2021.15.4.81
• 56. Puniach E., Bieda A., Ćwiąkała P., Kwartnik-Pruc A., Parzych P. Use of unmanned aerial vehicles (UAVs) for updating farmland cadastral data in areas subject to landslides. ISPRS Int. J. Geo-Inf. 2018, 7, 331. https://doi.org/10.3390/ijgi7080331.
• 57. Crommelinck S., Bennett R., Gerke M., Yang M.Y., Vosselman G. Contour detection for UAV-based cadastral mapping. Remote Sens. 2017, 9, 171. https://doi.org/10.3390/rs9020171.
• 58. Crommelinck S., Höfle B., Koeva M.N., Yang M.Y., Vosselman G. Interactive cadastral boundary delineation from UAV data. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2018, IV-2, 81–88. https://doi.org/10.5194/isprs-annals-IV-2-81-2018.
• 59. Šafář V., Potůčková M., Karas J., Tlustý J., Štefanová E., Jančovič M., Cígler Žofková D. The Use of UAV in Cadastral Mapping of the Czech Republic. ISPRS Int. J. Geo-Inf. 2021, 10, 380. https://doi.org/10.3390/ijgi10060380
• 60. Pyka K. Wiącek P. Guzik M. Surveying with photogrammetric unmanned aerial vehicles. Arch. of Photogramm. Cartogr. and Remote Sens. 2020, 32, 79–102. https://doi.org/10.14681/apcrs.2020.0004
• 61. Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE). OJ EU L 108/1 of 25.04.2007.
• 62. Act of 4 March 2010 on spatial information infrastructure. Journal of Laws 2010 No. 76 Item 489 as Amended. https://isap.sejm.gov.pl/isap.nsf/download.xsp/WDU20100760489/U/D20100489Lj.pdf (Accessed: 04.06.2024).
• 63. Regulation of the Minister of Development, Labor and Technology of 23 July 2021 on the database of topographic objects and the basic map. Journal of Laws 2021 Item 1385. https://isap.sejm.gov.pl/isap.nsf/download.xsp/WDU20210001385/O/D20211385.pdf (Accessed: 04.06.2024).
• 64. Regulation of the Minister of Development of 18 August 2020 on technical standards for the performance of geodetic situational and height measurements as well as the development and transfer of the results of these measurements to the state geodetic and cartographic resource. Journal of Laws 2020 Item 1429. https://isap.sejm.gov.pl/isap.nsf/download.xsp/WDU20200001429/O/D20201429.pdf (Accessed: 04.06.2024).
• 65. Ferrer-González E., Agüera-Vega F., CarvajalRamírez F., Martínez-Carricondo P. UAV photogrammetry accuracy assessment for corridor mapping based on the number and distribution of ground control points. Remote Sens. 2020, 12, 2447. https://doi.org/10.3390/rs12152447.
• 66. He G.B., Li L.L. Research and application of LIDAR technology in cadastral surveying and mapping. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2020, 43, 33–37. https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-33-2020.