Abstract
Convolution neural network (CNN) is an effective and popular deep learning method which automatically learns complicated non-linear mapping from original inputs to given labels or ground truth through a series of convolutional layers. This study focuses on detecting landslides from high-resolution optical satellite images using CNN-based methods, providing opportunities for recognizing latent landslides and updating large-scale landslide inventory with high accuracy and time efficiency. Considering the variety of landslides and complicated backgrounds, attention mechanisms originated from the human visual system are developed for boosting the CNN to extract more distinctive feature representations of landslides from backgrounds. As deep learning needs a large number of labeled data to train a learning model, we manually prepared a landslide dataset which is located in the Bijie city, China. In the dataset, 770 landslides, including rock falls, rock slides, and a few debris slides, were interpreted by geologists from the satellite images and digital elevation model (DEM) data and further checked by fieldwork. The landslide data was separated into a training set that trains the attention boosted CNN model and a testing set that evaluates the performance of the model with a ratio of 2:1. The experimental results showed that the best F1-score of landslide detection reached 96.62%. The results also proved that the performance of our spatial-channel attention mechanism was fairly over other recent attention mechanisms. Additionally, the effectiveness of predicting new potential landslides with high efficiency based on our dataset is demonstrated.
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References
Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135
Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano river, Niigata Prefecture, Japan. Landslides 1:73–81
Barlow J, Martin Y, Franklin S (2003) Detecting translational landslide scars using segmentation of Landsat ETM+ and DEM data in the northern Cascade Mountains, British Columbia. Can J Remote Sens 29:510–517
Bellotti F, Bianchi M, Colombo D, Ferretti A and Tamburini A (2014) Advanced InSAR techniques to support landslide monitoring. Mathematics of planet earth, Springer, pp 287–290
Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazards Earth Syst Sci 13:2815–2831
Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40:834–848
Chollet F (2017) Xception: deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258
Cruden DM (1991) A simple definition of a landslide. Bull Eng Geol Environ 43:27–29
Cruden D, Varnes D (1996) Landslide types and processes. Turner, AK & Schuster, RL (eds.) Landslides: investigation and mitigation, 36–75. Transportation Research Board Special Report 247
Czikhardt R, Papco J, Bakon M, Liscak P, Ondrejka P, Zlocha M (2017) Ground stability monitoring of undermined and landslide prone areas by means of sentinel-1 multi-temporal InSAR, case study from Slovakia. Geosciences 7:87
Ding A, Zhang Q, Zhou X and Dai B (2016) Automatic recognition of landslide based on CNN and texture change detection. 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), IEEE, pp 444–448
Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen C-W, Han Z and Pham BTJL (2019) Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides 1–18
Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730
Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66:327–343
Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J (2019) Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens 11:196
Girshick R (2015) Fast r-cnn. Paper presented at the Proceedings of the IEEE international conference on computer vision, pp 1440–1448
He K, Zhang X, Ren S and Sun J (2016) Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861
Hu J, Shen L and Sun G (2018) Squeeze-and-excitation networks. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141
Huang G, Liu Z, Van Der Maaten L and Weinberger KQ (2017) Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708
Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ and Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:1602.07360
Ioffe S and Szegedy CJapa (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167
Kayastha P, Dhital MR, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408
Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74:17–28
Krizhevsky A, Sutskever I and Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, pp 1097–1105
Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26:1477–1491
Lee S (2007) Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environ Geol 52:615–623
Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123:225–234
Park J, Woo S, Lee J-Y and Kweon IS (2018) Bam: bottleneck attention module. arXiv preprint arXiv:1807.06514
Rau J-Y, Jhan J-P, Rau R-J (2013) Semiautomatic object-oriented landslide recognition scheme from multisensor optical imagery and dem. IEEE Trans Geosci Remote Sens 52:1336–1349
Ren S, He K, Girshick R and Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Advances in neural information processing systems, pp 91–99
Ronneberger O, Fischer P and Brox T (2015) U-net: convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241
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 15:5–19
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D and Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, pp 618–626
Simonyan K and Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Sun Q, Zhang L, Ding X, Hu J, Li Z, Zhu J (2015) Slope deformation prior to Zhouqu, China landslide from InSAR time series analysis. Remote Sens Environ 156:45–57
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A (2015) Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9
Szegedy C, Vanhoucke V, Ioffe S, Shlens J and Wojna Z (2016) Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826
Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, Wang X, Tang X (2017) Residual attention network for image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3156–3164
Woo S, Park J, Lee J-Y and So Kweon I (2018) Cbam: convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), pp 3–19
Xie S, Girshick R, Dollár P, Tu Z and He K (2017) Aggregated residual transformations for deep neural networks. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492–1500
Yu F and Koltun VJapa (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122
Yu H, Ma Y, Wang L, Zhai Y and Wang X (2017) A landslide intelligent detection method based on cnn and rsg_r. 2017 IEEE International Conference on Mechatronics and Automation (ICMA), IEEE, pp 40–44
Zagoruyko S and Komodakis N (2016) Wide residual networks. arXiv preprint arXiv:1605.07146
Acknowledgments
We thank the geologists from the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection for the indoor interpretation of landslide regions from satellite images; we thank the local officials who helped us check those potential landslides; we thank Yunhong Tian from Wuhan University and the University of Waterloo helped polish the English.
Funding
This work was supported by the National Key Research and Development Program of China, Grant No. 2018YFB0505003.
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Ji, S., Yu, D., Shen, C. et al. Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks. Landslides 17, 1337–1352 (2020). https://doi.org/10.1007/s10346-020-01353-2
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DOI: https://doi.org/10.1007/s10346-020-01353-2