Landslide detection based on shipborne images and deep learning models: a case study in the Three Gorges Reservoir Area in China

[1]  Bingbo Gao,et al.  Multi-modal fusion of satellite and street-view images for urban village classification based on a dual-branch deep neural network , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[2]  J.M.V. Grzybowski,et al.  Convolutional neural networks applied to semantic segmentation of landslide scars , 2021, CATENA.

[3]  J. Gong,et al.  On the applicability of satellite SAR interferometry to landslide hazards detection in hilly areas: a case study of Shuicheng, Guizhou in Southwest China , 2021, Landslides.

[4]  Thimmaiah Gudiyangada Nachappa,et al.  Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach , 2021, Landslides.

[5]  S. Gelly,et al.  An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2020, ICLR.

[6]  Basant Kumar,et al.  Review on remote sensing methods for landslide detection using machine and deep learning , 2020, Trans. Emerg. Telecommun. Technol..

[7]  Peng Gao,et al.  Landslide mapping with remote sensing: challenges and opportunities , 2020, International Journal of Remote Sensing.

[8]  Weile Li,et al.  Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks , 2020, Landslides.

[9]  C. Juang,et al.  Geohazards in the three Gorges Reservoir Area, China – Lessons learned from decades of research , 2019, Engineering Geology.

[10]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[11]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[12]  Jieping Ye,et al.  Object Detection in 20 Years: A Survey , 2019, Proceedings of the IEEE.

[13]  Zhong Lu,et al.  Multi-Temporal Loess Landslide Inventory Mapping with C-, X- and L-Band SAR Datasets - A Case Study of Heifangtai Loess Landslides, China , 2018, Remote. Sens..

[14]  Yanhui Lin,et al.  Deep diagnostics and prognostics: An integrated hierarchical learning framework in PHM applications , 2018, Appl. Soft Comput..

[15]  Shoichiro Kojima,et al.  Landslide detection based on height and amplitude differences using pre- and post-event airborne X-band SAR data , 2018, Natural Hazards.

[16]  Yu Liu,et al.  A review of semantic segmentation using deep neural networks , 2017, International Journal of Multimedia Information Retrieval.

[17]  Farhad Samadzadegan,et al.  Deep learning decision fusion for the classification of urban remote sensing data , 2018 .

[18]  Thomas Oommen,et al.  A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[19]  Xiao Xiang Zhu,et al.  Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources , 2017, IEEE Geoscience and Remote Sensing Magazine.

[20]  Zenghui Wang,et al.  Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review , 2017, Neural Computation.

[21]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[22]  Zhong Lu,et al.  Detecting seasonal landslide movement within the Cascade landslide complex (Washington) using time-series SAR imagery. , 2016 .

[23]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[24]  Fei Ma,et al.  Reservoir-induced landslides and risk control in Three Gorges Project on Yangtze River, China , 2016 .

[25]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Domenico Calcaterra,et al.  Landslide detection integrated system (LaDIS) based on in-situ and satellite SAR interferometry measurements , 2016 .

[27]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Laura Longoni,et al.  Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives , 2014, Remote. Sens..

[29]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[30]  Ivan Laptev,et al.  Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  S. Leroueil,et al.  The Varnes classification of landslide types, an update , 2014, Landslides.

[32]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[33]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[34]  William Stafford Noble,et al.  Support vector machine , 2013 .

[35]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[36]  Jianhua Gong,et al.  Shipborne Mobile Photogrammetry for 3D Mapping and Landslide Detection of the Water-Level Fluctuation Zone in the Three Gorges Reservoir Area, China , 2021, Remote. Sens..

[37]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[38]  George A. Tsihrintzis,et al.  Machine Learning Paradigms , 2015 .

[39]  Bagher Shirmohammadi,et al.  Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method , 2014 .

[40]  Vaishali Ganganwar,et al.  An overview of classification algorithms for imbalanced datasets , 2012 .

[41]  Fawu Wang,et al.  Landslide disaster mitigation in Three Gorges Reservoir, China , 2009 .