Improving the Accuracy of Landslide Detection in “Off-site” Area by Machine Learning Model Portability Comparison: A Case Study of Jiuzhaigou Earthquake, China

Qiao, Huijie; Zhou, Yi; Wang, Shixing; Wang, Futao; Wang, Hongjie

doi:10.3390/rs11212530

Cited by 36 publications

(13 citation statements)

References 67 publications

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“…Deep learning techniques do not require hand-crafted features as they perform automatic feature engineering directly from satellite imagery. Hu et al [42] used landslide images from multiple seasons from LandSat satellite and digital elevation model (DEM) data to extract landslides using three ML models. Prakash et al [11] used Sentinel-2 images, DEM data from light detection and ranging (Lidar), and a derived normalized difference vegetation index (NDVI) layer to map landslides using a modified U-Net model [48], [49].…”

Section: A Landslide Mappingmentioning

confidence: 99%

Constructing a Large-Scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates

Nagendra

Kifer

Mirus

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Preparation and mitigation efforts for widespread landslide hazards can be aided by a large-scale, well-labeled landslide inventory with high location accuracy. Recent smallscale studies for pixel-wise labeling of potential landslide areas in remotely-sensed images using deep learning (DL) showed potential but were based on data from very small, homogeneous regions with unproven model transferability. In this paper we consider a more realistic and practical setting for large-scale heterogeneous landslide data collection and DL-based labeling. In this setting, remotely sensed images are collected sequentially in temporal batches, where each batch focuses on images from a particular ecoregion, but different batches can focus on different ecoregions with distinct landscape characteristics. For such a scenario, we study the following questions: (1) How well do DL models trained in homogeneous regions perform when they are transferred to different ecoregions, (2) Does increasing the spatial coverage in the data improve model performance in a given ecoregion (even when the extra data do not come from the ecoregion), and (3) Can a landslide pixel labeling model be incrementally updated with new data, but without access to the old data and without losing performance on the old data (so that researchers can share models obtained from proprietary datasets)? We address these questions by extending the Learning without Forgetting framework, which is used for incremental training of image classification models, to the setting of incremental training of semantic segmentation models (e.g., identifying all landslide pixels in an image). We call the resulting extension Task-Specific Model Updates (TSMU). TSMU semantic segmentation framework consists of an encoder shared by all ecoregions to capture the similarities between them, and ecoregion-specific decoders to capture the nuances of each ecoregion. This framework is continually updated using a threestage training procedure for each new addition of an ecoregion without having to revisit data from old ecoregions and without losing performance on them.A national compilation of landslide inventories by the U.S. Geological Survey (USGS) was used to develop database for this study. We focused on space-visible landslides within four ecoregions. These landslides were manually identified and labeled using high-resolution satellite images from Google Earth. The database contains 496 labeled and georeferenced pre-event/postevent images pairs, which corresponding to 1,918 landslide records in the USGS landslide inventory. Using the TSMU framework, we conduct extensive experiments on four ecoregions in the United States to address the aforementioned questions.

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Section: A Landslide Mappingmentioning

confidence: 99%

Constructing a Large-Scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates

Nagendra

Kifer

Mirus

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Deep learning, a branch of ML, has also been used for landslide mapping [20], [29]- [34]. Deep learning techniques are more efficient in terms of automatic feature engineering directly from satellite imagery.…”

Section: B Deep Learning Approaches and Limitations Of Homogeneous Training Datamentioning

confidence: 99%

“…Deep learning techniques are more efficient in terms of automatic feature engineering directly from satellite imagery. [29] used landslide images from multiple seasons from LandSat satellite and DEM data to extract landslides using three ML models. [32] used Sentinel-2 images, DEM data from Lidar, and a derived NDVI layer to map landslides using a modified U-Net model [7].…”

Section: B Deep Learning Approaches and Limitations Of Homogeneous Training Datamentioning

confidence: 99%

Constructing a Large-scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates

Nagendra¹,

Manjunatha²,

Kifer³

et al. 2022

Preprint

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“…Commonly used methods include support vector machines (SVM), decision trees (DTs), random forest (RF), and artificial neural networks (ANN) [20]. Among them, [21,22] have demonstrated that RF exhibited stable portability on new data compared to SVM and ANN. Piralilou et al [23] proposed a landslide detection method connecting the result of OOA with logistic regression, multilayer perceptron, and RF by the Dempster-Shafer theory.…”

Section: Introductionmentioning

confidence: 99%

Landslide Detection Based on ResU-Net with Transformer and CBAM Embedded: Two Examples with Geologically Different Environments

Xu¹,

2022

Remote Sensing

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An efficient method of landslide detection can provide basic scientific data for emergency command and landslide susceptibility mapping. Compared to a traditional landslide detection approach, convolutional neural networks (CNN) have been proven to have powerful capabilities in reducing the time consumed for selecting the appropriate features for landslides. Currently, the success of transformers in natural language processing (NLP) demonstrates the strength of self-attention in global semantic information acquisition. How to effectively integrate transformers into CNN, alleviate the limitation of the receptive field, and improve the model generation are hot topics in remote sensing image processing based on deep learning (DL). Inspired by the vision transformer (ViT), this paper first attempts to integrate a transformer into ResU-Net for landslide detection tasks with small datasets, aiming to enhance the network ability in modelling the global context of feature maps and drive the model to recognize landslides with a small dataset. Besides, a spatial and channel attention module was introduced into the decoder to effectually suppress the noise in the feature maps from the convolution and transformer. By selecting two landslide datasets with different geological characteristics, the feasibility of the proposed model was validated. Finally, the standard ResU-Net was chosen as the benchmark to evaluate the proposed model rationality. The results indicated that the proposed model obtained the highest mIoU and F1-score in both datasets, demonstrating that the ResU-Net with a transformer embedded can be used as a robust landslide detection method and thus realize the generation of accurate regional landslide inventory and emergency rescue.

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Improving the Accuracy of Landslide Detection in “Off-site” Area by Machine Learning Model Portability Comparison: A Case Study of Jiuzhaigou Earthquake, China

Cited by 36 publications

References 67 publications

Constructing a Large-Scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates

Constructing a Large-Scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates

Constructing a Large-scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates

Landslide Detection Based on ResU-Net with Transformer and CBAM Embedded: Two Examples with Geologically Different Environments

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