Journal ArticleDOI
Convolutional neural networks applied to semantic segmentation of landslide scars
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TLDR
The results indicate that the U-Net architecture has the potential to identify landslide scars, improving over previously published research on the topic for the same study region and the potential of the method to be applied in dynamic mapping systems for landslide scar identification.Abstract:
Landslides are considered to be among the most alarming natural hazards. Therefore, there is a growing demand for databases and inventories of these events worldwide, since they are a vital resource for landslide risk assessment applications. Given the recent advances in the field of image processing, the objective of this study is to evaluate the performance of a deep convolutional neural network architecture called U-Net for the mapping of landslide scars from satellite imagery. The question that drives the study is: can fully convolutional neural networks be successfully applied as the backbone of automatic frameworks for building landslide inventories, keeping or improving the identification accuracy and agility when compared to other methods? To seek for an answer to it, scenes from the Landsat-8 satellite of a region of Nepal were obtained and processed in order to compose a landslide image database that served as the basis for the training, validation and test of deep convolutional neural networks. The U-Net architecture was applied and the results indicate that it has the potential to identify landslide scars, improving over previously published research on the topic for the same study region. The validation process resulted in recall, precision and F1-score values of 0.74, 0.61 and 0.67, respectively, thus higher than those from previous studies using different methodologies. The results indicate the potential of the method to be applied in dynamic mapping systems for landslide scar identification, which paves the way to the composition and updating of landslide scar databases. These, in turn, can support a great deal of quantitative landslide susceptibility mapping methods that heavily rely on data to provide accurate results.read more
Citations
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Journal ArticleDOI
Landslide detection using deep learning and object-based image analysis
Omid Ghorbanzadeh,Hejar Shahabi,Alessandro Crivellari,Saeid Homayouni,Thomas Blaschke,Pedram Ghamisi +5 more
TL;DR: In this paper , the authors examined the feasibility of the integration framework of a DL model with rule-based object-based image analysis (OBIA) to detect landslides.
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Landslide susceptibility analyses using Random Forest, C4.5, and C5.0 with balanced and unbalanced datasets
TL;DR: Density, silt and clay content, and Atterberg’s limits were the most important geotechnical conditioning factors in the performed landslide susceptibility analyses.
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Automatic Detection of Coseismic Landslides Using a New Transformer Method
TL;DR: Both quantitative evaluation and visualization results show that Transformer is capable of outperforming CNNs in landslide detection.
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HADeenNet: A hierarchical-attention multi-scale deconvolution network for landslide detection
TL;DR: Wang et al. as mentioned in this paper proposed a hierarchical deconvolution network to detect landslides, which enlarges the input feature maps by a decoder operation and convolutes the enlarged feature maps to learn to detect landslide.
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Encoding Contextual Information by Interlacing Transformer and Convolution for Remote Sensing Imagery Semantic Segmentation
TL;DR: The ICTNet is devised to confront the deficiencies of the encoder–decoder architecture, and together with the devised encoder and decoder, the well-rounded context is captured and contributes to the inference most.
References
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Posted Content
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
Landslide inventory maps: New tools for an old problem
Fausto Guzzetti,Alessandro Cesare Mondini,Mauro Cardinali,Federica Fiorucci,Michele Santangelo,Kang-Tsung Chang +5 more
TL;DR: In this article, the authors outline the principles for landslide mapping, and review the conventional methods for the preparation of landslide maps, including geomorphological, event, seasonal, and multi-temporal inventories.
Journal ArticleDOI
Neocognitron: A Hierarchical Neural Network Capable of Visual Pattern Recognition
TL;DR: The operation of tolerating positional error a little at a time at each stage, rather than all in one step, plays an important role in endowing the network with an ability to recognize even distorted patterns.