A PATH GUIDED METHOD FOR QUICKLY LABELING ROADS IN SATELLITE IMAGES

Year 2021, Volume: 7 - Eurasiagraphics’20 Special Issue, 19 - 26, 16.10.2021

Samet Cengiz Özcan , Muhammed Abdullah Bülbül

https://doi.org/10.22531/muglajsci.908214

Abstract

Determining roads from satellite images has gained more research interest after the recent progress on data-heavy machine learning methods which are also accelerated by the increased amounts of accessible data. An important challenge of learning-based approaches is obtaining labeled data to train the systems. In this study, we propose a method for quickly labeling roads over satellite images of any desired location. Our method exploits the 2D path information obtained from OpenStreetMap, an online community-contributed source of geolocated information. In this environment, roads are roughly described as line segments without their exact shapes and sizes. Using this rough information, we propose a simple interactive user interface where users easily label the road boundaries over presented satellite images. Using our approach, it is possible to rapidly label regions with different road characteristics. Such an approach allows for training separate machine learning systems for different parts of the world which would be advantageous over training a single system to identify all kinds of roads.

Keywords

City Modeling, Satellite images, OpenStreetMap, Labeling, Machine Learning

References

• Yildirim, O., Cetin, M., Erdogan, M., Gurleyuk, N. and Bulbul, A. “Race on your street,” in Eurasia Graphics 2018, 2018.
• Bulbul, A. and Dahyot, R. “Populating virtual cities using social media,” Computer Animation and Virtual Worlds, vol. 28, no. 5, p. e1742, 2017.
• Bacher, U. and Mayer, H. “Automatic road extraction from irs satellite images in agricultural and desert areas,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 35, p. B3, 2004.
• Kocaman, S., Zhang, L., Gruen, A. and Poli, D. “3d city modeling from high-resolution satellite images,” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 36, no. 1/W41, 2006.
• Wang, Y., Tian, Y., Tai, X. and Shu, L. “Extraction of main urban roads from high resolution satellite images by machine learning,” Asian Conference on Computer Vision. Springer, 2006, pp. 236–245.
• Deepan, P., Abinaya, S., Haritha, G. and Iswarya, V. “Road recognition from remote sensing imagery using machine learning,” International Research Journal of Engineering and Technology, vol. 5, no. 3, pp.3677–3683, 2018.
• Xu, Y., Xie, Z., Feng, Y. and Chen, Z. “Road extraction from high-resolution remote sensing imagery using deep learning,” Remote Sensing, vol. 10, no. 9, p. 1461, 2018.
• Zhang, J., Chen, L., Wang, C., Zhuo, L., Tian, Q. and Liang, X. “Road recognition from remote sensing imagery using incremental learning,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 11,pp. 2993–3005, 2017.
• Taspınar, A.“Using convolutional neural networks to detect features in satellite images,” https://ataspinar.com, 2017.
• Wang, J., Song, J., Chen, M. and Yang, Z. “Road network extraction: A neural-dynamic framework based on deep learning and a finite state machine,” International Journal of Remote Sensing, vol. 36, no. 12, pp.3144–3169, 2015.
• Xia, W.,Zhang, Y.-Z., Liu, J., Luo, L. and Yang, K. “Road extraction from high resolution image with deep convolution network—a case study of gf-2 image,” Multidisciplinary Digital Publishing Institute Proceedings, vol. 2, no. 7, p. 325, 2018.
• Wang, W., Yang, N., Zhang, Y., Wang, F., Cao, T. and Eklund, P. “A review of road extraction from remote sensing images,” Journal of traffic and transportation engineering (english edition), vol. 3, no. 3, pp. 271–282,2016.
• Cao, J., Song, C., Song, S., Xiao, F. and Peng, S. “Lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments,” Sensors, vol. 19, no. 14, p. 3166, 2019.
• Malladi, C. “Detection of objects in satellite images using supervised and unsupervised learning methods,” Faculty of Computing Blekinge Institute of Technology, 2017.
• Dai, J., Zhu, T., Zhang, Y., Ma, R. and Li, W. “Lane-level road extraction from high-resolution optical satellite images,” Remote Sensing, vol. 11,no. 22, p. 2672, 2019.
• Azimi, S. M., Fischer, P., Körner, M. and Reinartz, P. “Aerial lanenet: Lane-marking semantic segmentation in aerial imagery using wavelet-enhanced cost-sensitive symmetric fully convolutional neural networks,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 5,pp. 2920–2938, 2018.
• Mattyus, G., Wang, S., Fidler, S. and Urtasun, R. “Enhancing road maps by parsing aerial images around the world,” Proceedings of the IEEE International Conference on Computer Vision, pp. 1689–1697, 2015.
• Demir, I., Hughes, F., Raj, A., Dhruv, K., Muddala, S. M., Garg, S., Doo, B. and Raskar, R. “Generative street addresses from satellite imagery,” ISPRS International Journal of Geo-Information, vol. 7, no. 3, p. 84, 2018.
• Ozcan, S. C. and Kaya, H. “An analysis of travelling salesman problem utilizing hill climbing algorithm for a smart city touristic search on openstreetmap (osm),” in 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE, pp. 1–5, 2018.
• Wiki openstreetmap, “About openstreetmap,” https://wiki.openstreetmap.org, 2020.
• Bulbul, A. “3d city modeling from accessible online sources,” 8. Ulusal Savunma Uygulamaları Modelleme ve Simülasyon Konferansı (USMOS), pp. 315–326, 2019.
• Google Earth earth.google, “Create stories and maps,” https://earth.google.com/web/search, 2021.
• Made in Bocholt madeinbocholt, “Virtuelles Stadtmodell auf Basis von Geodaten,” https://madeinbocholt.de/virtuelles-stadtmodell-auf-basis-von-geodaten, 2021.