João G. P. Rodrigues
ABSTRACT
3D maps of urban environments are useful in various fields, from cellular network planning to urban planning and climatology. We show that 3D urban maps can be extracted from received satellite signals that are attenuated by obstacles, such as buildings, from low-accuracy GNSS data, crowdsourced opportunistically from standard smartphones during their user's uncontrolled daily commute trips. Our proposal incorporates position inaccuracies in the calculations, and the diversity of collection conditions of crowdsourced GNSS positions is used to mitigate bias and noise from the data. A binary classification model is trained and evaluated on multiple urban scenarios. Our results show that the generalization accuracy for a Random Forest classifier lies between 79% and 91%, demonstrating the potential of the proposed method for building 3D maps for wide urban areas. In the demo, we show multiple 3D visualizations of the various processing stages, which can be viewed interactively using Google Earth, allowing hands-on exploration of the work.
- M Brasebin, J Perret, S Mustiè re, and C Weber. 2012. Measuring the impact of 3D data geometric modeling on spatial analysis: Illustration with Skyview factor. In Usage, Usability, and Utility of 3D City Models - European COST Action TU0801, T. Leduc, G. Moreau, and R. Billen (Eds.), Vol. 02001. EDP Sciences, Les Ulis, France, 02001. https://doi.org/10.1051/3u3d/201202001Google Scholar
- Andrew T. Irish, Jason T. Isaacs, Francois Quitin, Joao P. Hespanha, and Upamanyu Madhow. 2014. Probabilistic 3D mapping based on GNSS SNR measurements. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings . IEEE, Florence, Italy, 2390--2394. https://doi.org/10.1109/ICASSP.2014.6854028Google Scholar
- Kihwan Kim, Jay Summet, Thad Starner, Daniel Ashbrook, Mrunal Kapade, and Irfan Essa. 2008. Localization and 3D reconstruction of urban scenes using GPS. In Proceedings - International Symposium on Wearable Computers, ISWC. IEEE, Pittsburgh, PA, USA, 11--14. https://doi.org/10.1109/ISWC.2008.4911576Google ScholarDigital Library
- João G. P. Rodrigues and Ana Aguiar. 2019. Extracting 3D Maps from Crowdsourced GNSS Skyview Data. In Proceedings of the 25th Annual International Conference on Mobile Computing and Networking (MobiCom '19). ACM, Los Cabos, Mexico. https://doi.org/10.1145/3300061.3345456Google Scholar
- João G. P. Rodrigues, João P. Pereira, and Ana Aguiar. 2017. Impact of Crowdsourced Data Quality on Travel Pattern Estimation. In Proceedings of the First ACM Workshop on Mobile Crowdsensing Systems and Applications - CrowdSenSys '17. ACM Press, New York, New York, USA, 38--43. https://doi.org/10.1145/3139243.3139254Google ScholarDigital Library
Index Terms
- Demo: Extracting 3D Maps from Crowdsourced GNSS Skyview Data
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