Airborne lidar data classification in complex urban area using random forest: a case study of Bergama, Turkey

Abstract

Airborne Light Detection and Ranging (LiDAR) data have been increasingly used for classification of urban areas in the last decades. Classification of urban areas is especially crucial to separate the area into classes for urban planning, mapping, and change detection monitoring purposes. In this study, an airborne LiDAR data of a complex urban area from Bergama District, İzmir, Turkey were classified into four classes; buildings, trees, asphalt road, and ground. Random Forest (RF) supervised classification method is selected as classification algorithm and pixel-wise classification was performed. Ground truth of the area was generated by digitizing classes into features to select training data and to validate the results. The selected study area from Bergama district is complex in urban planning of buildings, road, and ground. The buildings are very close to each other, and trees are also very close to buildings and sometimes cover the rooftops of buildings. The most challenging part of this study is to generate ground truth in such a complex area. According to the obtained classification results, the overall accuracy of the results is found as 70, 20%. The experimental results showed that the algorithm promises reliable results to classify airborne LiDAR data into classes in a complex urban area.

Keywords

• Random Forest
• LiDAR
• Classification
• Complex Urban Area

References

1. Belgiu, M. and Drǎguț L. (2016). Random forests in remote sensing: a review of applications and future directions. ISPRS J. Photogramm. Remote Sens., 114, pp. 24-31.
2. Breiman, L. (2001). “Random Forests.” Machine Learning 45: 5–32.
3. Canaz S., Aliefendioğlu Y. and Tanrıvermiş H. (2017). Change detection using Landsat images and an analysis of the linkages between the change and property tax values in the Istanbul Province of Turkey. Journal of Environmental Management. Vol. 200:446-45.
4. Chehata, N., Li, G. and Mallet, C. (2009). Airborne LIDAR feature selection for urban classification using random forests. Geomat. Inform. Sci. Wuhan Univ. 38, 207–212.
5. Dittrich, A., Weinmann, M. and Hinz, S. (2017). Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data. ISPRS J. Photogramm. 126, 195–208.
6. Charaniya, A.P., Manduchi, R. and Lodha, S.K. (2004). Supervised parametric classification of aerial LiDAR data. In Proceedings of 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’04), Washington, DC.
7. Chen, W., Li, X., Wang, Y., Chen, G. and Liu S. (2014). Forested landslide detection using LiDAR data and the random forest algorithm: a case study of the Three Gorges, China. Remote Sens. Environ. 152 (2014), pp. 291-301.
8. GDAL/OGR contributors (2018). GDAL/OGR Geospatial Data Abstraction software Library. The Open Source Geospatial Foundation. URL http://gdal.org

9. Guan, H., Li, J., Chapman, M., Deng, F., Ji, Z. and Yang, X. (2013). Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests. International Journal of Remote Sensing, vol. 34, issue 14, pp. 5166-5186.
10. Guo, L., Chehata, N., Mallet, C. and Boukir, S. (2011). Relevance of airborne LiDAR and multispectral image data for urban scene classification using Random Forests. ISPRS Journal of Photogrammetry and Remote Sensing, 66 (1) , pp. 56-66
11. Kayı A. Erdoğan M. and Eker O. (2015). Results of LİDAR test performed by OPTECH HA-500 and RIEGL LMS-Q1560. Harita Dergisi, Volume 153, pp 42-46.
12. Lodha, S.K., Kreps, E.J., Helmbold, D.P. and Fitzpatrick, D. (2006). Aerial LiDAR data classification using support vector machines (SVM). The Third International Symposium on 3D Data Processing, Visualization, and Transmission pp. 567-574.
13. Ma L., Zhou M. and Li C. (2017). Land Covers Classification Based On Random Forest Method Using Features From Full-Waveform Lidar Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII- 2/W7, 2017 ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
14. Niemeyer, J., Rottensteiner, F. and Soergel, U. (2012). Conditional random fields for LiDAR point cloud classification in complex urban areas. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-3, pp. 263-26.
15. Optech, (2018), http://www.Optech.Com/Specification [Accessed on: 14 May 2018]
16. Richards, J.A. and Jia, X. (1999). Supervised Classification Techniques Remote Sensing Digital Image, Analysis, Springer-Verlag GmbH, Heidelberg (1999) pp. 193–247.
17. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blonde,l M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12:2825-2830.
18. Python Software Foundation. Python Language Reference, version 2.7. available at http://www.python.org. Retrieved on 15.04.2018.
19. Rodriguez-Galiano, V., Ghimire, B., Rogan, J., Chica- Olmo, M. and Rigol-Sanchez, J. (2012). An assessment of the effectiveness of a random forest classifier for land- cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67 (0), pp. 93-104.
20. Rottensteiner, F. and Briese C. (2002). A new method for building extraction in urban areas from high-resolution LiDAR data. Int Arch Photogramm Remote Sens Spat Inf Sci 34(3A):295–301
21. Yang, X. and Lo, C.P. (2002). Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area , International Journal of Remote Sensing, 23, pp. 1775— 1798.
22. Yu, X., Zhang, A., Hou, X., Li, M., and Xia, Y. (2013). Multi-temporal remote sensing of land cover change and urban sprawl in the coastal city of Yantai, China. International Journal of Digital Earth. Vol. 6, Supplement 2, 137-154.