Application of drone for landslide mapping, dimension estimation and its 3D reconstruction
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Dimension estimation of landslides is a major challenge while preparing the landslide inventory map, for which very high-resolution satellite data/aerial photography is required, which is very expensive. An alternative is the application of drones for such mapping. This study presents the utility of drone/unmanned aerial vehicle (UAV) for mapping and 3D reconstruction of two landslides near IIT Mandi, Himachal Pradesh. In this study, we used DJI Phantom 3 Advanced drone to collect high-resolution images of landslides. Features in the images were automatically detected, described, and matched among photographs using scale invariant feature transform (SIFT) technique. The 3D position and orientation of the cameras and the XYZ location of each feature in the photographs was estimated using bundle block adjustment. This resulted in sparse 3D point cloud, which was densified using Clustering View for Multi-View Stereo (CMVS) algorithm. Finally, surface reconstruction was done using Poisson Surface Reconstruction method, which was visualised in open source software CloudCompare. The 3D model, generated from drone images, was validated using field measurements of some objects, and 3D surface created from total station. Various quantities i.e. width (length), height and perimeter were measured in the 3D drone model and verified with total station data. The differences among all the measured quantities for both the landslides are less than 5% keeping the measurements of total station as reference. The 3D reconstructed from the sets of photographs is very accurate giving the measurements up to cm level and even small objects could be easily identified. In addition, digital elevation model (DEM) of sub meter resolution could be easily generated and used for various applications. Hence drone-based imagery in combination with 3D scene reconstruction algorithms provide flexible and effective tools to map and monitor landslide apart from accurately assessing the dimensions of the landslides.
KeywordsDrone Landslide mapping, 3D reconstruction SfM Point cloud
The author would like to thank Mr. Abhay Guleria (M.S. Scholar), Mr. M. Naresh (Ph.D. Scholar), Mr. Lokesh Tungariya and Mr Rakesh Meena (B.Tech Students) and and Mr. Sudhanshu Gautam (Intern) of IIT Mandi, for their help during the field work and data collection using total station. This paper would not come out in such a better shape without the suggestive and critical comments of both the reviewers.
- Berger, M., Tagliasacchi, A., Seversky, L. M., Alliez, P., Levine, J. A., Sharf, A., et al. (2014). State of the art in surface reconstruction from point clouds. EUROGRAPHICS Star Reports, 1(1), 161–185.Google Scholar
- Fuhrmann, S., Langguth, F., & Goesele, M. (2014). MVE—a multi-view reconstruction environment. In Proceedings of the eurographics workshop on graphics and cultural heritage (pp. 11–18). Eurographics Association.Google Scholar
- Furukawa, Y., Curless, B., Seitz, S. M., & Szeliski, R. (2010). Towards Internet-scale multi-view stereo. In IEEE Computer Society conference on computer vision and pattern recognition (pp. 1434–1441). IEEE.Google Scholar
- Gupta, S. K., & Shukla, D. P., (2017). 3D reconstruction of a landslide by application of UAV and structure from motion. In 20th AGILE conference on geographic information science, 9–12 May 2017, Wageningen, The Netherlands. ISBN 978-90-816960-7-4. Accessible https://agile-online.org/index.php/conference/proceedings/proceedings-2017.
- Kumar, A., Mukherjee, A. B., & Krishna, A. P. (2017a). Application of conventional data mining techniques and web mining to aid disaster management. In A. V. Senthil Kumar (Ed.), Web usage mining techniques and applications across industries (pp. 138–167). IGI Global: Hershey, PA.CrossRefGoogle Scholar
- Poonam, C., Rana, N., Champati ray, P. K., Bisht, P., Bagri, D. S., Wasson, R. J., et al. (2017). Identification of landslide-prone zones in the geomorphically and climatically sensitive Mandakini valley, (central Himalaya), for disaster governance using the Weights of Evidence method. Geomorphology, 284, 41–52.CrossRefGoogle Scholar
- Saunders, G. M. (2014). Development of photogrammetric methods for landslide analysis. University of Oslo.Google Scholar
- Shukla, D. P., Gupta, S., Dubey, C. S., & Thakur, M. (2016). Geo-spatial technology for landslide hazard zonation and prediction. In M. Marghany (Ed.), Environmental applications of remote sensing (pp. 281–308). InTech.Google Scholar
- Siyahghalati, S., Saraf, A. K., Pradhan, B., Jebur, M. N., & Tehrany, M. S. (2016). Rule-based semi-automated approach for the detection of landslides induced by 18 September 2011 Sikkim, Himalaya, earthquake using IRS LISS3 satellite images. Geomatics, Natural Hazards and Risk, 7(1), 326–344.CrossRefGoogle Scholar
- Triggs, B., Triggs, B., McLauchlan, P., Hartley, R., & Fitzgibbon, A. (2000). Bundle adjustment—a modern synthesis. In Vision algorithms: Theory and practice, LNCS, pp. 298–375.Google Scholar
- Tsutsui, K., Rokugawa, S., Nakagawa, H., Miyazaki, S., Cheng, C. T., Shiraishi, T., et al. (2007). Detection and volume estimation of large-scale landslides based on elevation-change analysis using DEMs extracted from high-resolution satellite stereo imagery. IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1681–1696.CrossRefGoogle Scholar
- Van Westen, C. J., Ghosh, S., Jaiswal, P., Martha, T. R., & Kuriakose, S. L. (2013). From landslide inventories to landslide risk assessment; an attempt to support methodological development in India. In C. Margottini, P. Canuti, & K. Sassa (Eds.), Landslide science and practice: Landslide inventory and susceptibility and hazard zoning (Vol. 1, pp. 3–20). Berlin: Springer.CrossRefGoogle Scholar
- Wieczorek, G. F. (1984). Preparing a detailed landslide-inventory map for hazard evaluation and reduction. Bulletin Association of Engineering Geologists, 21(3), 337–342.Google Scholar
- Wu, C. (2007). SiftGPU: A GPU implementation of scale invaraint feature transform (SIFT). http://cs.unc.edu/~ccwu/siftgpu.
- Wu, C. (2013). Towards linear-time incremental structure from motion. In Proceedings— 2013 international conference on 3D vision, 3DV 2013 (pp. 127–134). IEEE.Google Scholar
- Wu, C., Agarwal, S., Curless, B., & Seitz, S. M. (2011). Multicore bundle adjustment. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition (pp. 3057–3064). IEEE.Google Scholar