Analysis Of The Positioning Accuracy Of Geotagged Photos Taken With Mobile Devices In Various Terrain Conditions
DOI:
https://doi.org/10.18172/cig.6565Keywords:
photography, geotagging, GIS, GNSS, Mobile deviceAbstract
Currently, most mobile devices can capture geotagged photos—i.e., images to which the location of capture is assigned. Despite extensive literature on the use of geotagging, there is a limited number of studies addressing the accuracy of the recorded locations. Therefore, this research was undertaken to assess the positional accuracy of geotagged photos, defined, among other metrics, by the mean unit error of the assigned coordinates. This article presents the results of test measurements conducted using various mobile devices to determine situational coordinates within the applicable coordinate system. The study discusses the satellite systems currently in use, as well as the measurement technologies that influence geolocation accuracy in smartphones and cameras equipped with a geotagging feature. Test measurements involved comparing the coordinates embedded in geotagged photos with those obtained using a high-precision GNSS receiver. Depending on the device and technology used, the mean unit location errors ranged from 4.0 metres to nearly 50 metres. These findings highlight the low precision of such devices in determining exact positions. To explore ways of improving accuracy, additional tests were carried out using various features and applications available on different devices, assessing their impact on location determination based on geotagged photos. Notably, the use of the GPS Test application for position stabilisation reduced mean unit errors by nearly 45%. The results of this study led to the development of recommendations aimed at enabling the determination of a mobile device’s X and Y coordinates with an accuracy of several metres. This level of precision may be sufficient for many practical applications and presents a cost-effective alternative to expensive GPS receivers, which require specialised geodetic knowledge for professional use.
Downloads
References
Ai, Q., Maciuk, K., Lewinska, P., Borowski, L. 2021. Characteristics of Onefold Clocks of GPS. Galileo. BeiDou and GLONASS Systems. Sensors 21, 2396. https://doi.org/10.3390/s21072396
barbeau/gpstest. 2025. Available at: https://github.com/barbeau/gpstest (last access 09/01/2025)
Benson, B. 2016. PokemonGo. Createspace Independent Publishing Platform, p. 56.
Bogusz, J., Klos, A., Pokonieczny, K. 2019. Optimal Strategy of a GPS Position Time Series Analysis for Post-Glacial Rebound Investigation in Europe. Remote Sens. 11, 1209. https://doi.org/10.3390/rs11101209
BTSearch. 2025. Available at: https://beta.btsearch.pl/ (last access 09/01/2025).
Chavez, D.J., Knap, N.E., McCollum, D.G. 2004. Assessing National Forest Visitors’ Comprehension of International Symbols for Communicating Outdoor Recreation Messages Parks &Recreation 22 (3).
Dunn, O.J. 1964. Multiple comparisons using rank sums. Technometrics 6 (3), 241-252
Dz.U. 2024 poz. 342. 2012. Rozporządzenie Rady Ministrów z dnia 15 października 2012 r. w sprawie państwowego systemu odniesień przestrzennych. Available at: https://isap.sejm.gov.pl/isap.nsf/download.xsp/WDU20240000342/O/D20240342.pdf (last access: 09/06/2025)
ElQadi, M.M., Lesiv, M., Dyer, A.G., Dorin, A. 2020. Computer vision-enhanced selection of geo-tagged photos on social network sites for land cover classification. Environmental Modelling & Software 128. https://doi.org/10.1016/j.envsoft.2020.104696
Fan, Y., Mandal, M.S.H., Nakabayashi, M., Hosaka, T. 2024. Visitor experience with biodiversity in tropical rainforests: A global-scale assessment using social media data. Biological Conservation 293. https://doi.org/10.1016/j.biocon.2024.110590
Foltête, J.C., Ingensand, J., Blanc, N. 2020. Coupling crowd-sourced imagery and visibility modelling to identify landscape preferences at the panorama level. Landscape and Urban Planning 197. https://doi.org/10.1016/j.landurbplan.2020.103756
García-Palomares, J.C., Gutiérrez, J., Mínguez, C. 2015. Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS. Applied Geography 63, 408-417. https://doi.org/10.1016/j.apgeog.2015.08.002
Geoportal. 2025. Available at: https://www.geoportal.gov.pl/ (last access 09/01/2025).
Golder, S., Huberman, B. 2006. Usage Patterns of Collaborative Tagging Systems. J. Information Science 32, 198-208. https://doi.org/10.1177/0165551506062337
GPS Test. 2025. Available at: https://play.google.com/store/apps/details?id=com.chartcross.gpstest (last access 09/01/2025).
Hofmann-Wellenhof, B., Lichtenegger, H., Wasle, E. 2008. GNSS – Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and more. Springer Vien, Austria.
Hu, Y., Gao, S. Janowicz, K., Yu, B., Li, W., Prasad, S. 2015. Extracting and understanding urban areas of interest using geotagged photos. Computers. Environment and Urban Systems 54. 240-254. https://doi.org/10.1016/j.compenvurbsys.2015.09.001
Huang, R. 2023. Analyzing national parks visitor activities using geotagged social media photos. Journal of Environmental Management 330. https://doi.org/10.1016/j.jenvman.2022.117191
Humphreys, L., Liao, T. 2011. Mobile Geotagging: Reexamining Our Interactions with Urban Space. Journal of Computer-Mediated Communication 16 (3), 407-423. https://doi.org/10.1111/j.1083-6101.2011.01548.x
Ihamäki, P. 2014. The potential of treasure hunt games to generate positive emotions in learners: Experiencing local geography and history using GPS devices. International Journal of Technology Enhanced Learning 6 (1), 5-20. http://doi.org/10.5555/2608743.2608744
Ihamäki, P. 2015. User Experience of Geocaching and Its Application to Tourism and Education (doctoral dissertation). Annales Universitatis Turkuensis B404, 249 s. http://doi.org/10.13140/RG.2.1.3202.3205
Kaplan, E.D., Hegarty C. 2017. Understanding GPS/GNSS: Principles and Applications, Third Edition, Artech House: Boston USA. 993 pp.
Kenny, U., Regan, Á., Hearne, D., O'Meara, C. 2021. Empathising. defining and ideating with the farming community to develop a geotagged photo app for smart devices: A design thinking approach. Agricultural Systems 194. https://doi.org/10.1016/j.agsy.2021.103248
Kobylarczyk, J., Cebulska, M., Baziak, B., Bodziony, M. 2024. Assessment of thermal conditions in urban interiors on the example of the Cracow University of Technology campus. Materiały Budowlane 628. Issue 12.pp. 235-244. http://doi.org/10.15199/33.2024.12.25
Kumaran, V.J., Mohidem, N.A., Che’Ya, N.N., Ilahi, W.F.F., Shah, J.A., Sahwee, Z., Yusof, N., Omar, M.H. 2024. How can aerial imagery and vegetation indices algorithms monitor the geotagged crop? The Egyptian Journal of Remote Sensing and Space Sciences 27 (4), 628-636. https://doi.org/10.1016/j.ejrs.2024.08.003
Lee, H., Seo, B., Koellner, T., Lautenbach, S. 2019. Mapping cultural ecosystem services 2.0 – Potential and shortcomings from unlabeled crowd sourced images. Ecological Indicators 96. Part 1. pp. 505-515. https://doi.org/10.1016/j.ecolind.2018.08.035
Lee, L., Jones, M., Ridenour, G.S., Bennett, S.J., Majors, A.C., Melito, B.L., Wilson, M.J. 2016. Comparison of Accuracy and Precision of GPS-Enabled Mobile Devices. IEEE International Conference on Computer and Information Technology (CIT), Nadi, Fiji, pp. 73-82, http://doi.org/10.1109/CIT.2016.94
Li, X., Pan, L., Yu, W. 2021. Assessment and Analysis of the Four-Satellite QZSS Precise Point Positioning and the Integrated Data Processing With GPS. IEEE Access 9, 116376-116394. http://doi.org/10.1109/ACCESS.2021.3106050
Lissai, G. 2006. Assisted GPS solution in cellular networks. Master Thesis. Rochester Institute of Technology. https://repository.rit.edu/theses/6475
Lu, J., Guo, X., Su, C. 2020. Global capabilities of BeiDou Navigation Satellite System. Satell Navig 1. https://doi.org/10.1186/s43020-020-00025-9
Merry, K., Bettinger, P. 2019. Smartphone GPS accuracy study in an urban environment. PLoS ONE 14 (7), e0219890. https://doi.org/10.1371/journal.pone.0219890
Navarrete-Hernandez, P., Luneke, A., Truffello, R., Fuentes, L. 2023. Planning for fear of crime reduction: Assessing the impact of public space regeneration on safety perceptions in deprived neighborhoods. Landscape and Urban Planning 237. https://doi.org/10.1016/j.landurbplan.2023.104809
NGA. 2014. Department Of Defense World Geodetic System 1984. Its Definition and Relationships with Local Geodetic Systems. National Geospatial-Intelligence Agency (NGA) Standardization Document. Office of Geomatics.
NIST/SEMATECH e-Handbook of Statistical Methods. 2025. Available at: http://www.itl.nist.gov/div898/handbook/ (last access 07/08/2025)
OpenStreetMap. 2025. Available at: https://www.openstreetmap.org (last access 09/01/2025).
Orsi, F., Geneletti, D. 2013. Using geotagged photographs and GIS analysis to estimate visitor flows in natural areas. Journal for Nature Conservation 21 (5), 359-368. https://doi.org/10.1016/j.jnc.2013.03.001
Park, M., Luo, J., Collins, R.T., Liu, Y. 2014. Estimating the camera direction of a geotagged image using reference images. Pattern Recognition 47 (9), 2880-2893. https://doi.org/10.1016/j.patcog.2014.03.002
Payntar, N.D., Hsiao, W.L., Covey, R.A., Grauman, K. 2021. Learning patterns of tourist movement and photography from geotagged photos at archaeological heritage sites in Cuzco. Peru. Tourism Management 82. https://doi.org/10.1016/j.tourman.2020.104165
QGIS Python Plugins Repository. 2025. Available at: https://plugins.qgis.org/plugins/ImportPhotos/#plugin-about (last access 09/01/2025).
Ryser, E., Spichiger, H., Jaquet-Chiffelle, D.O. 2024. Geotagging accuracy in smartphone photography. Forensic Science International: Digital Investigation 50 (Supplement), 301813. https://doi.org/10.1016/j.fsidi.2024.301813
Saadeldin, M., O'Hara, R., Zimmermann, J., Mac Namee, B., Green, S. 2022. Using deep learning to classify grassland management intensity in ground-level photographs for more automated production of satellite land use maps. Remote Sensing Applications: Society and Environment 26. https://doi.org/10.1016/j.rsase.2022.100741
Samany, N.N. 2019. Automatic landmark extraction from geo-tagged social media photos using deep neural network. Cities 93, 1-12. https://doi.org/10.1016/j.cities.2019.04.012
Santra, A., Mahato, S., Dan. S., Bose, A. 2019. Precision of satellite based navigation position solution: A review using NavIC data. Journal of Information and Optimization Sciences 40 (8), 1683–1691. https://doi.org/10.1080/02522667.2019.1703264
Sathianarayanan, M., Hsu, P.H., Chang, C.C. 2024. Extracting disaster location identification from social media images using deep learning. International Journal of Disaster Risk Reduction 104. https://doi.org/10.1016/j.ijdrr.2024.104352
Spatialreference. 2025. Available at: https://spatialreference.org/ref/epsg/2178/ (last access 30/09/2025).
Tavani, S., Billi, A., Corradetti, A., Mercuri, M., Bosman, A., Cuffaro, M., Seers, T., Carminati, E. 2022. Smartphone assisted fieldwork: Towards the digital transition of geoscience fieldwork using LiDAR-equipped iPhones. Earth-Science Reviews 227, 103969, https://doi.org/10.1016/j.earscirev.2022.103969
Tsui, J.B.Y. 2005. Basic GPS Concept. In K. Chang (Ed.), Fundamentals of Global Positioning System Receivers: A Software Approach. 2nd ed. Wiley & Sons, pp. 7-29. https://doi.org/10.1002/0471712582.ch2
Tsutsumida, N., Funada, S. 2023. Mapping cherry blossom phenology using a semi-automatic observation system with street level photos. Ecological Informatics 78, 102314. https://doi.org/10.1016/j.ecoinf.2023.102314
Uznański, A. 2017. Comparative analysis of the quality of RTN measurements related to all reference networks in Poland. Research and Technical Papers of Association for Transportation Engineers in Cracow 1, 167-181.
Vu, H.Q., Li, G., Law, R., Ye, B.H. 2015. Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos. Tourism Management 46, 222-232. https://doi.org/10.1016/j.tourman.2014.07.003
Xu, Z., Chen, L., Chen, G. 2015. Topic based context-aware travel recommendation method exploiting geotagged photos. Neurocomputing 155, 99-107. https://doi.org/10.1016/j.neucom.2014.12.043
Zaragozí, B., Trilles, S., Carrion, D., Pérez-Albert, M.Y. 2020. A geotagged image dataset with compass directions for studying the drivers of farmland abandonment. Data in Brief 33. https://doi.org/10.1016/j.dib.2020.106340
Zhang, S., Zhou, W. 2018. Recreational visits to urban parks and factors affecting park visits: Evidence from geotagged social media data. Landscape and Urban Planning 180, 27-35. https://doi.org/10.1016/j.landurbplan.2018.08.004
Zheng, Y., Zheng, F., Yang, C., Nie, G., Li, S. 2022. Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions. Remote Sens 14. https://doi.org/10.3390/rs14184640
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Anna Szafarczyk, Beata Baziak, Marek Bodziony
This work is licensed under a Creative Commons Attribution 4.0 International License.
The authors retain copyright of articles and authorize Cuadernos de Investigación Geográfica / Geographical Research Letters the first publication. They are free to share and redistribute the article without obtaining permission from the publisher as long as they give appropriate credit to the editor and the journal.
Self-archiving is allowed too. In fact, it is recommendable to deposit a PDF version of the paper in academic and/or institutional repositories.
It is recommended to include the DOI number.
This journal is licensed under a Creative Commons Attribution 4.0 International License
