Abstract
Landslides are catastrophic natural hazards that often lead to loss of life, property damage, and economic disruption. Image-based landslide investigations are crucial for determining landslide susceptibility and risk. In practice, satellite images have been widely utilized for such investigations; however, they still require significant labor and time resources. In this study, we propose an image-based two-phase data-driven framework for detecting and segmenting landslide regions using satellite images. In phase I, an object detection algorithm, Faster-RCNN, is trained to detect the landslide location within the large-scale satellite images. The bounding boxes of each landslide location are proposed and visualized. In phase II, we crop the satellite images into small images using the location information of the bounding boxes. Next, we use a boundary detection algorithm to identify the boundary information of each detected loess landslide to strengthen the segmentation performance. Finally, we improve the architecture of the segmentation U-Net by integrating additional inception blocks with dilation to enhance the landslide segmentation performance. A total of 150 local loess landslide occurrences in northern China are selected as our case study to validate the effectiveness, efficiency, and universality of the proposed two-phase framework. Segmentation of loess landslides is considered a challenging task due to the intrinsic nature of vague boundary information. The proposed framework is compared with the conventional U-Net and other recent benchmarking landslide segmentation algorithms. Computational results indicate that the proposed framework produces more accurate segmentation of loess landslides compared with the other tested benchmarking algorithms.
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References
Donati N, Sharma A, Ovsjanikov M (2020) Deep geometric functional maps: robust feature learning for shape correspondence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8592–8601)
Fan X, Scaringi G, Korup O, West AJ, van Westen CJ, Tanyas H, Huang R (2019) Earthquake induced chains of geologic hazards: patterns, mechanisms, and impacts. Rev Geophys 57(2):421–503
Feng W, Sui H, Huang W, Xu C, An K (2018) Water body extraction from very high-resolution remote sensing imagery using deep U-Net and a superpixel-based conditional random field model. IEEE Geosci Remote Sens Lett 16(4):618–622
Ghimire S, Deo RC, Raj N, Mi J (2019) Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms. Appl Energy 253, 113541
Girshick R, Donahue J, Darrell T, Malik J (2015) Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell 38(1):142–158
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778)
He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN. In Proceedings of the IEEE international conference on computer vision (pp. 2961–2969)
Ji S, Yu D, Shen C, Li W, Xu Q (2020) Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks. Landslides 17(6):1337–1352
Kamnitsas K, Ferrante E, Parisot S, Ledig C, Nori AV, Criminisi A, Glocker B (2016) DeepMedic for brain tumor segmentation. In International workshop on Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries (pp. 138–149). Springer, Cham.
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105
Li H, Xu Q, He Y, Fan X, Li S (2020) Modeling and predicting reservoir landslide displacement with deep belief network and EWMA control charts: a case study in Three Gorges Reservoir. Landslides 17(3):693–707
Liu Y, Cen C, Che Y, Ke R, Ma Y, Ma Y (2020) Detection of maize tassels from UAV RGB imagery with Faster R-CNN. Remote Sensing 12(2):338
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431–3440)
Mahmoud A, Mohamed S, El-Khoribi R, Abdelsalam H (2020) Object detection using adaptive mask RCNN in optical remote sensing images. Int J Intell Eng Syst 13:65–76
Milletari F, Navab N, Ahmadi SA (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV) (pp. 565–571) IEEE
Mohan A, Singh AK, Kumar B, Dwivedi R (2020) Review on remote sensing methods for landslide detection using machine and deep learning. Trans Emerg Telecommun Technol e3998
Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Rueckert D (2018) Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999
Ouyang T, Kusiak A, He Y (2017) Modeling wind-turbine power curve: a data partitioning and mining approach. Renewable Energy 102:1–8
Ouyang T, He Y, Li H, Sun Z, Baek S (2019) Modeling and forecasting short-term power load with copula model and deep belief network. IEEE Transactions on Emerging Topics in Computational Intelligence 3(2):127–136
Ouyang T, Huang H, He Y, Tang Z (2020) Chaotic wind power time series prediction via switching data-driven modes. Renewable Energy 145:270–281
Peng J, Fan Z, Wu D, Zhuang J, Dai F, Chen W, Zhao C (2015) Heavy rainfall triggered loess–mudstone landslide and subsequent debris flow in Tianshui, China. Eng Geol 186:79–90
Peng J, Sun P, Igwe O (2018a) Loess caves, a special kind of geo-hazard on loess plateau, northwestern China. Eng Geol 236:79–88
Peng D, Xu Q, Liu F, He Y, Zhang S, Qi X, Zhang X (2018b) Distribution and failure modes of the landslides in Heitai terrace, China. Eng Geol 236:97–110
Pradhan B, Al-Najjar HA, Sameen MI, Mezaal MR, Alamri AM (2020) Landslide detection using a saliency feature enhancement technique from LiDAR-derived DEM and Orthophotos. IEEE Access 8:121942–121954
Pravitasari AA, Iriawan N, Almuhayar M, Azmi T, Fithriasari K, Purnami SW, Ferriastuti W (2020) UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation. Telkomnika 18(3):1310–1318
Ren S, He K, Girshick R, Sun J (2016) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234–241) Springer, Cham
Rosi A, Tofani V, Tanteri L, Stefanelli CT, Agostini A, Catani F, Casagli N (2018) The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution. Landslides 15(1):5–19
Shi J, Chang Y, Xu C, Khan F, Chen G, Li C (2020) Real-time leak detection using an infrared camera and Faster R-CNN technique. Comput & Chem Eng 135:106780
Syzdykbayev M, Karimi B, Karimi HA (2020) Persistent homology on LiDAR data to detect landslides. Remote Sensing of Environment 246:111816
Su, Z., Chow, J.K., Tan, P.S. et al. (2021). Deep convolutional neural network–based pixel-wise landslide inventory mapping. Landslides 18, 1421–1443 . https://doi.org/10.1007/s10346-020-01557-6
Wang Z, Wang X, Yang W, Xiao Y, Liu Y, Chen L (2020) yNet: a multi-input convolutional network for ultra-fast simulation of field evolvement. arXiv preprint arXiv:2012.10575
Xu C (2015) Preparation of earthquake-triggered landslide inventory maps using remote sensing and GIS technologies: principles and case studies. Geosci Front 6(6):825–836
Xu Q, Li H, He Y, Liu F, Peng D (2019) Comparison of data-driven models of loess landslide runout distance estimation. Bull Eng Geol Env 78(2):1281–1294
Yildiz C, Acikgoz H, Korkmaz D, Budak U (2021) An improved residual-based convolutional neural network for very short-term wind power forecasting. Energy Convers Manag 228:113731
Yu B, Chen F, Xu C (2020) Landslide detection based on contour-based deep learning framework in case of national scale of Nepal in 2015. Comput Geosci 135:104388
Zeng J, Wang F, Deng J, Qin C, Zhai Y, Gan J, Piuri V (2020) Finger vein verification algorithm based on fully convolutional neural network and conditional random field. IEEE Access 8:65402–65419
Zhang L, Liu A, Xiao J, Taylor P (2020) Dual Encoder Fusion U-Net (DEFU-Net) for Cross-manufacturer Chest X-ray Segmentation. arXiv preprint arXiv:2009.10608
Zhong Z, Kim Y, Plichta K, Allen BG, Zhou L, Buatti J, Wu X (2019) Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks. Med Phys 46(2):619–633
Funding
This research is supported by the Major Program of the National Natural Science Foundation of China (Grant No. 41790445), the Key Program of National Natural Science Foundation of China (Grant No. 41630640), the Opening fund of State Key Laboratory of Geohazard Prevention and Geo-environment Protection (Chengdu University of Technology) (Grant No. SKLGP2021K014), and the Project of remote sensing identification and monitoring of geological hazards in Sichuan province, CN (2020) (Grant No. 510201202076888).
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Li, H., He, Y., Xu, Q. et al. Detection and segmentation of loess landslides via satellite images: a two-phase framework. Landslides 19, 673–686 (2022). https://doi.org/10.1007/s10346-021-01789-0
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DOI: https://doi.org/10.1007/s10346-021-01789-0