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Utilization of low-cost GNSS RTK receiver for accurate GIS mapping in urban environment

  • Marek Halaj and Michal Kačmařík ORCID logo EMAIL logo
Published/Copyright: December 17, 2024
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Abstract

The main objective of the work was to evaluate positioning performance of low-cost GNSS receiver u-blox ZED-F9P with patch antenna in GIS mapping scenarios. The whole mapping kit consisted of the mentioned receiver, a smartphone and two mobile applications. Thirty testing points with different levels of sky view were temporarily set up in an urban environment. Real-Time Kinematic (RTK) technique was used to estimate their position with the mentioned low-cost receiver and geodetic grade Trimble R10 receiver. Coordinates obtained from two independent measurement campaigns were compared to reference positions computed by rapid static relative technique. Both receivers provided a similar level of positioning correctness except two testing points where the geodetic grade GNSS receiver showed large errors in the first campaign. With an exclusion of these two points, both receivers delivered mean horizontal distances from the reference positions slightly exceeding 0.04 m and standard deviations oscillating around 0.05 m. In case of height estimates, mean differences from the reference values were at the level of 0.02 m for the Trimble R10 receiver and 0.07 m for the u-blox receiver, with standard deviations around 0.08 m reached by both tested devices. Secondly, areas of four polygons in the same urban environment were measured. Relative differences from reference values of areas ranged from 0.01 % to 0.46 %, with the Trimble receiver being slightly better in this task.


Corresponding author: Michal Kačmařík, Department of Geoinformatics, Faculty of Mining and Geology, VŠB – Technical University of Ostrava, 708 00, Ostrava, Czech Republic, E-mail: 

Funding source: Faculty of Mining and Geology, VSB – Technical University Ostrava, Czech Republic

Award Identifier / Grant number: Grant of SGS No. SP2023/084

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: Conceptualization, M.H. and M.K.; methodology, M.H. and M.K.; validation, M.H.; formal analysis, M.K.; resources, M.K.; data acquistion, M.H. and M.K.; writing—original draft preparation, M.H. and M.K.; writing—review and editing, M.K.; visualization, M.H.; supervision, M.K. The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: Work was supported by Grant of SGS No. SP2023/084 (Utilization of current GNSS devices for localization, navigation and research of transport applications), Faculty of Mining and Geology, VSB - Technical University Ostrava, Czech Republic.

  7. Data availability: The raw data can be obtained on request from the corresponding author.

References

1. Wing, M, Kellogg, L. Locating and mobile mapping techniques for forestry applications. Geogr Inf Sci 2004;10:2. https://doi.org/10.1080/10824000409480669.Search in Google Scholar

2. Klooster, D, Strout, N, Smith, D. GIS in the jungle: experiential environmental education (EEE) in Panama. J Environ Stud Sci 2022;12:164–76. https://doi.org/10.1007/s13412-021-00716-y.Search in Google Scholar PubMed PubMed Central

3. de Donatis, M, Alberti, M, Cesarini, C, Menichetti, M, Susini, S. Open source GIS for geological field mapping: research and teaching experience. PeerJ 2016;4. https://doi.org/10.7287/peerj.preprints.2258v3.Search in Google Scholar

4. Giardino, M, Perotti, L, Lanfranco, M, Perrone, G. GIS and geomatics for disaster management and emergency relief: a proactive response to natural hazards. Appl Geomat 2012;4:33–46. https://doi.org/10.1007/s12518-011-0071-z.Search in Google Scholar

5. Olyazadeh, R, Sudmeier-Rieux, K, Jaboyedoff, M, Derron, M-H, Devkota, S. An offline-online Web-GIS android application for fast data acquisition of landslide hazard and risk. Nat Hazards Earth Syst Sci 2017;17:549–61. https://doi.org/10.5194/nhess-17-549-2017.Search in Google Scholar

6. Li, S, Cai, H, Kamat, VR. Uncertainty-aware geospatial system for mapping and visualizing underground utilities. Autom Constr 2015;53:105–19. https://doi.org/10.1016/j.autcon.2015.03.011.Search in Google Scholar

7. Fitterer, J, Nelson, TA, Nathoo, F. Predictive crime mapping. Police Pract Res 2015;16:121–35. https://doi.org/10.1080/15614263.2014.972618.Search in Google Scholar

8. Jelokhani-Niaraki, M, Bastami, MR, Yazdanpanah, DQ, Hajiloo, F, Sadeghi-Niaraki, A. A volunteered geographic information system for monitoring and managing urban crimes: a case study of Tehran, Iran. Police Pract Res 2020;21:547–61. https://doi.org/10.1080/15614263.2019.1644175.Search in Google Scholar

9. Ghorbanzadeh, O, Blaschke, T, Gholamnia, K, Meena, SR, Tiede, D, Aryal, J. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens 2019;11:196. https://doi.org/10.3390/rs11020196.Search in Google Scholar

10. Immitzer, M, Vuolo, F, Atzgerber, C. First experience with sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens 2016;8:166. https://doi.org/10.3390/rs8030166.Search in Google Scholar

11. Inglada, J, Arias, M, Tardy, B, Hagolle, O, Valero, S, Morin, D, et al.. Assessment of an operational system for crop type map production using high temporal and spatial resolution satellite optical imagery. Remote Sens 2015;7:12356–79. https://doi.org/10.3390/rs70912356.Search in Google Scholar

12. Mishra, PK, Rai, A, Rai, SC. Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India. Egypt J Remote Sens Space Sci 2020;23:133–43. https://doi.org/10.1016/j.ejrs.2019.02.001.Search in Google Scholar

13. Näsi, R, Honkavaara, E, Lyytikäinen-Saarenmaa, P, Blomqvist, M, Litkey, P, Hakala, T, et al.. Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sens 2015;7:15467–93. https://doi.org/10.3390/rs71115467.Search in Google Scholar

14. Al-Hamad, A, El-Sheimy, N. Smartphones based mobile mapping systems. ISPRS Archives 2014;40:29–34. https://doi.org/10.5194/isprsarchives-XL-5-29-2014.Search in Google Scholar

15. Nowak, M, Dziób, K, Ludwisiak, L, Chmiel, J. Mobile GIS applications for environmental field surveys: a state of the art. Global Ecol Conserv 2020;23:e01089. https://doi.org/10.1016/j.gecco.2020.e01089.Search in Google Scholar

16. Teunissen, PJG, Montenbruck, O. Springer handbook of global navigation satellite systems. Springer International Publishing; 2017. In press.10.1007/978-3-319-42928-1Search in Google Scholar

17. Pendão, C, Ferreira, A, Moreira, A, Martins, C, Silva, H. Challenges in characterization of GNSS precise positioning systems for automotive. In: 10th International Conference on Localization and GNSS, ICL-GNSS 2020, Tampere, Finland; 2020. In press.Search in Google Scholar

18. Gao, Z, Ge, M, Li, Y, Chen, Q, Zhang, Q, Niu, X, et al.. Odometer, low-cost inertial sensors, and four-GNSS data to enhance PPP and attitude determination. GPS Solut 2018;22:57. https://doi.org/10.1007/s10291-018-0725-y.Search in Google Scholar

19. Kaczmarek, A, Rohm, W, Klingbeil, L, Tchórzewski, J. Experimental 2D extended Kalman filter sensor fusion for low-cost GNSS/IMU/Odometers precise positioning system. Measurement 2022;193:110963. https://doi.org/10.1016/j.measurement.2022.110963.Search in Google Scholar

20. Li, T, Zhang, H, Gao, Z, Chen, Q, Niu, X. High-accuracy positioning in urban environments using single-frequency multi-GNSS RTK/MEMSIMU integration. Remote Sens 2018;10:205. https://doi.org/10.3390/rs10020205.Search in Google Scholar

21. Schwieger, V, Gläser, A. Possibilities of low cost GPS technology for precise geodetic applications. In: FIG working week 2005 and GSDI-8. Cairo, Egypt; 2005. April 16-21, 2005.Search in Google Scholar

22. Schwieger, V. High-sensitivity GPS – an availability, reliability and accuracy test. In: FIG working week 2008. Stockholm, Sweden; 2008. 14-19 June 2008.Search in Google Scholar

23. Glabsch, J, Heunecke, O, Schuhbäck, S. A low-cost PDGNSS-based sensor network for landslide monitoring – challenges, possibilities, and prospects. Int J Digital Earth 2010;3:365–83. https://doi.org/10.1080/17538947.2010.489622.Search in Google Scholar

24. Dabove, P, de Agostino, M, Manzino, A. Mass-market L1 GPS receivers for mobile mapping applications: a novel approach. In: 24th international technical meeting of the satellite division of the institute of navigation 2011, ION GNSS 2011, Portland, USA, september 20 – 23, 2011; 2011.Search in Google Scholar

25. Zhang, L, Stange, M, Schwieger, V. Automatic low-cost GPS monitoring system using WLAN communication. In: FIG working week 2012. Rome, Italy; 2012. 6-10 May 2012.Search in Google Scholar

26. Xu, JW, He, GW. Research on a positioning application by RTK technique and with goGPS. Adv Mater Res 2013;798:549–52. https://doi.org/10.4028/www.scientific.net/AMR.798-799.549.Search in Google Scholar

27. Biagi, L, Grec, F, Negretti, M. Low-cost GNSS receivers for local monitoring: experimental simulation, and analysis of displacements. Sensors 2016;16:2140. https://doi.org/10.3390/s16122140.Search in Google Scholar PubMed PubMed Central

28. Notti, D, Cina, A, Manzino, A, Colombo, A, Bendea, IH, Mollo, P, et al.. Low-cost GNSS solution for continuous monitoring of slope instabilities applied to Madonna Del Sasso Sanctuary (NW Italy). Sensors 2020;20:289. https://doi.org/10.3390/s20010289.Search in Google Scholar PubMed PubMed Central

29. Šegina, E, Peternel, T, Urbančič, T, Realini, E, Zupan, M, Jež, J, et al.. Monitoring surface displacement of a deep‐seated landslide by a low‐cost and near real‐time GNSS system. Remote Sens 2020;12:1–26. https://doi.org/10.3390/rs12203375.Search in Google Scholar

30. Hamza, V, Stopar, B, Sterle, O, Pavlovcic-Preseren, P. A cost-effective GNSS solution for continuous monitoring of landslides. Remote Sens 2023a;15:2287. https://doi.org/10.3390/rs15092287.Search in Google Scholar

31. Ogutcu, S, Alcay, S, Duman, H, Ozdemir, BN, Konukseven, C. Static and kinematic PPP-AR performance of low-cost GNSS receiver in monitoring displacements. Adv Space Res 2023;72:4795–808. https://doi.org/10.1016/j.asr.2023.09.025.Search in Google Scholar

32. Odolinski, R, Teunissen, P. Low-cost, high-precision, single-frequency GPS–BDS RTK positioning. GPS Solut 2017;21:1315–30. https://doi.org/10.1007/s10291-017-0613-x.Search in Google Scholar

33. Garrido-Carretero, MS, de Lacy-Pérez de los Cobos, MC, Borque-Arancón, MJ, Ruiz-Armenteros, AM, Moreno-Guerrero, R, Gil-Cruz, AJ. Low-cost GNSS receiver in RTK positioning under the standard ISO-17123-8: a feasible option in geomatics. Measurement 2019;137:168–78. https://doi.org/10.1016/j.measurement.2019.01.045.Search in Google Scholar

34. ISO. International standard ISO 17123-8: 2007 – optics and optical instruments – field procedures for testing geodetic and surveying instruments – part 8: GNSS field measurement systems in real-time kinematic (RTK), ISO. Geneva, Switzerland: International Standardization Organization; 2007.Search in Google Scholar

35. Hamza, V, Stopar, B, Sterle, O. Testing the performance of multi-frequency low-cost GNSS receivers and antennas. Sensors 2021;21:2029. https://doi.org/10.3390/s21062029.Search in Google Scholar PubMed PubMed Central

36. Hamza, V, Stopar, B, Sterle, O, Pavlovcic-Preseren, P. Low-cost dual-frequency GNSS receivers and antennas for surveying in urban areas. Sensors 2023b;23:2861. https://doi.org/10.3390/s23052861.Search in Google Scholar PubMed PubMed Central

37. Li, X, Gou, H, Li, X, Shen, Z, Lyu, H, Zhou, Y, et al.. Performance analysis of frequency-mixed PPP-RTK using low-cost GNSS chipset with different antenna configurations. Satell Navigation 2023a;4:26. https://doi.org/10.1186/s43020-023-00116-3.Search in Google Scholar

38. Li, L, Yuan, Y, Zhang, P. On low-cost GNSS observables under different grades of antennas: receiver-related biases and RTK results. Measurement 2023b;214:112771. https://doi.org/10.1016/j.measurement.2023.112771.Search in Google Scholar

39. Wielgocka, N, Hadas, T, Kaczmarek, A, Marut, G. Feasibility of using low-cost dual-frequency GNSS receivers for land surveying. Sensors 2021;21:1956. https://doi.org/10.3390/s21061956.Search in Google Scholar PubMed PubMed Central

40. Zumberge, JF, Heflin, MB, Jefferson, DC, Watkins, MM, Webb, FH. Precise point positioning for the efficient and robust analysis of GPS data from large networks. J Geophys Res Solid Earth 1997;102:5005–17. https://doi.org/10.1029/96JB03860.Search in Google Scholar

41. Kazmierski, K, Dominiak, K, Marut, G. Positioning performance with dual-frequency low-cost GNSS receivers. J Appl Geodesy 2023;17:255–67. https://doi.org/10.1515/jag-2022-0042.Search in Google Scholar

42. Tomastik, J, Everett, T. Static positioning under tree canopy using low-cost GNSS receivers and adapted RTKLIB software. Sensors 2023;23:3136. https://doi.org/10.3390/s23063136.Search in Google Scholar PubMed PubMed Central

43. Robustelli, U, Cutugno, M, Pugliaon, G. Low-cost GNSS and PPP-RTK: investigating the capabilities of the u-blox ZED-F9P module. Sensors 2023;23:6074. https://doi.org/10.3390/s23136074.Search in Google Scholar PubMed PubMed Central

44. Li, X, Zhang, X, Ge, M. Regional reference network augmented precise point positioning for instantaneous ambiguity resolution. J Geodesy 2011;85:151–8. https://doi.org/10.1007/s00190-010-0424-0.Search in Google Scholar

45. Janos, D, Kuras, P, Ortyl, L. Evaluation of low-cost RTK GNSS receiver in motion under demanding conditions. Measurement 2022;201:111647. https://doi.org/10.1016/j.measurement.2022.111647.Search in Google Scholar

46. Okoh, D, Obafaye, A, Dare-Idowu, O, Rabiu, B, Kashcheyev, A, Cesaroni, C, et al.. Assessment of the performance of the TOPGNSS and ANN-MB antennas for ionospheric measurements using low-cost u-blox GNSS receivers. Geodesy Geodyn 2023;15:291–301. https://doi.org/10.1016/j.geog.2023.11.002.Search in Google Scholar

47. Pan, Y, Kłopotek, G, Crocetti, L, Weinacker, R, Sturn, T, See, L, et al.. Determination of high-precision tropospheric delays using crowdsourced smartphone GNSS data. Atmos Meas Tech 2024;17:4303–16. https://doi.org/10.5194/amt-17-4303-2024.Search in Google Scholar

48. Chen, L, Chai, H, Zheng, N, Wang, M, Xiang, M. Feasibility and performance evaluation of low-cost GNSS devices for sea level measurement based on GNSS-IR. Adv Space Res 2023;72:4651–62. https://doi.org/10.1016/j.asr.2023.07.031.Search in Google Scholar

49. Takasu, T. RTKLIB: open source program package for RTK-GPS. In: Proc. FOSS4G 2009. Tokyo, Japan; 2009. November 2, 2009.Search in Google Scholar

50. Prange, L, Arnold, D, Dach, R, Brockmann, E, Kalarus, MS, Stefan, S, et al.. CODE product series for the IGS MGEX project. Bern, Switzerland: Astronomical Institute, University of Bern; 2023.Search in Google Scholar

Received: 2024-07-16
Accepted: 2024-11-23
Published Online: 2024-12-17
Published in Print: 2025-07-28

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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