Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks

Lei, Tao; Zhang, Yuxiao; Lv, Zhiyong; Li, Shuying; Liu, Shigang; Nandi, Asoke K.

doi:10.1109/lgrs.2018.2889307

Cited by 196 publications

(112 citation statements)

References 14 publications

(26 reference statements)

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“…We expand upon such studies by exploring the application of LiDAR, a variety of predictor variables and RF machine learning over a large spatial extent, which is uncommon in the literature. Although this study focuses on probabilistic mapping using the RF traditional machine learning method, is should be noted that deep learning methods that rely on convolutional neural networks have been explored for slope failure mapping and predictive tasks in several recent studies [25,29,30,[56][57][58][59][60][61].…”

Section: Mapping Slope Failures and Susceptibilitymentioning

confidence: 99%

Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt

Maxwell

Sharma²,

Kite

et al. 2020

Remote Sensing

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The probabilistic mapping of landslide occurrence at a high spatial resolution and over a large geographic extent is explored using random forests (RF) machine learning; light detection and ranging (LiDAR)-derived terrain variables; additional variables relating to lithology, soils, distance to roads and streams and cost distance to roads and streams; and training data interpreted from high spatial resolution LiDAR-derivatives. Using a large training set and all predictor variables, an area under the receiver operating characteristic (ROC) curve (AUC) of 0.946 is obtained. Our findings highlight the value of a large training dataset, the incorporation of a variety of terrain variables and the use of variable window sizes to characterize the landscape at different spatial scales. We also document important variables for mapping slope failures. Our results suggest that feature selection is not required to improve the RF modeling results and that incorporating multiple models using different pseudo absence samples is not necessary. From our findings and based on a review of prior studies, we make recommendations for high spatial resolution, large-area slope failure probabilistic mapping.

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Section: Mapping Slope Failures and Susceptibilitymentioning

confidence: 99%

Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt

Maxwell

Sharma²,

Kite

et al. 2020

Remote Sensing

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“…DL-based change detection methods have been applied to different targets such as urban [46][47][48][49], land use/land cover [50][51][52], and landslides [53], among others. Peng et al [54] proposed a subdivision of DL-based change detection methods that considered three units of analysis: (1) feature [55][56][57]; (2) patch [58][59][60][61]; and (3) image [62,63]. In the case of image-based DL change detection, the algorithms learn the segmentation of changes directly from bi-temporal image pairs, avoiding the negative effects caused when using pixel patches [54].…”

Section: Introductionmentioning

confidence: 99%

“…In the case of image-based DL change detection, the algorithms learn the segmentation of changes directly from bi-temporal image pairs, avoiding the negative effects caused when using pixel patches [54]. In this approach, the U-Net architecture has been successfully employed [63,64]. Among the DL algorithms, convolutional neural networks (CNN) are one of the leading types of architectures [22].…”

Section: Introductionmentioning

confidence: 99%

Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional Neural Networks

et al. 2020

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Mapping deforestation is an essential step in the process of managing tropical rainforests. It lets us understand and monitor both legal and illegal deforestation and its implications, which include the effect deforestation may have on climate change through greenhouse gas emissions. Given that there is ample room for improvements when it comes to mapping deforestation using satellite imagery, in this study, we aimed to test and evaluate the use of algorithms belonging to the growing field of deep learning (DL), particularly convolutional neural networks (CNNs), to this end. Although studies have been using DL algorithms for a variety of remote sensing tasks for the past few years, they are still relatively unexplored for deforestation mapping. We attempted to map the deforestation between images approximately one year apart, specifically between 2017 and 2018 and between 2018 and 2019. Three CNN architectures that are available in the literature—SharpMask, U-Net, and ResUnet—were used to classify the change between years and were then compared to two classic machine learning (ML) algorithms—random forest (RF) and multilayer perceptron (MLP)—as points of reference. After validation, we found that the DL models were better in most performance metrics including the Kappa index, F1 score, and mean intersection over union (mIoU) measure, while the ResUnet model achieved the best overall results with a value of 0.94 in all three measures in both time sequences. Visually, the DL models also provided classifications with better defined deforestation patches and did not need any sort of post-processing to remove noise, unlike the ML models, which needed some noise removal to improve results.

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“…In recent years, machine learning algorithms have become popular for mapping landslide damage using primarily optical imagery [26][27][28]. Nevertheless, although these approaches can yield a good accuracy for the classification of damage caused by a landslide, the strong dependency of these algorithms on extensive and high-quality training data hampers their applicability to rapid disaster response scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images

et al. 2020

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The rapid and accurate mapping of large-scale landslides and other mass movement disasters is crucial for prompt disaster response efforts and immediate recovery planning. As such, remote sensing information, especially from synthetic aperture radar (SAR) sensors, has significant advantages over cloud-covered optical imagery and conventional field survey campaigns. In this work, we introduced an integrated pixel-object image analysis framework for landslide recognition using SAR data. The robustness of our proposed methodology was demonstrated by mapping two different source-induced landslide events, namely, the debris flows following the torrential rainfall that fell over Hiroshima, Japan, in early July 2018 and the coseismic landslide that followed the 2018 Mw6.7 Hokkaido earthquake. For both events, only a pair of SAR images acquired before and after each disaster by the Advanced Land Observing Satellite-2 (ALOS-2) was used. Additional information, such as digital elevation model (DEM) and land cover information, was employed only to constrain the damage detected in the affected areas. We verified the accuracy of our method by comparing it with the available reference data. The detection results showed an acceptable correlation with the reference data in terms of the locations of damage. Numerical evaluations indicated that our methodology could detect landslides with an accuracy exceeding 80%. In addition, the kappa coefficients for the Hiroshima and Hokkaido events were 0.30 and 0.47, respectively.Keywords: landslide damage detection; the 2018 torrential rain event in hiroshima; Japan; the 2018 Mw6.7 hokkaido earthquake; synthetic aperture radar (SAR) intensity imagery

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Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks

Cited by 196 publications

References 14 publications

Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt

Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt

Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional Neural Networks

A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images

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