Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection

Ghorbanzadeh, Omid; Xu, Yonghao; Ghamis, Pedram; Kopp, Michael; Kreil, David

doi:10.48550/arxiv.2206.00515

Cited by 15 publications

(17 citation statements)

References 74 publications

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“…Furthermore, mention should be made of the interesting study by Ghorbanzadeh et al [44], whose objective was to analyze landslides in various susceptible areas of several Asian countries: Japan, India, Nepal, and Taiwan, from remote sensing applying various deep learning algorithms. Specifically, 11 models were applied: U-Net, ResUNet, PSPNet, ContextNet, DeepLab-v2, DeepLab-v3+, FCN-8s, LinkNet, FRRN-A, FRRN-B, and SQNet.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador

et al. 2022

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Natural hazards generate disasters and huge losses in several aspects, with landslides being one of the natural risks that have caused great impacts worldwide. The aim of this research was to explore a method based on machine learning to evaluate susceptibility to rotational landslides in an area near Cuenca city, Ecuador, which has a high incidence of these phenomena, mainly due to its environmental conditions, and in which, however, such studies are scarce. The implemented method consisted of an artificial neural network multilayer perceptron (ANN MLP), generated with the neuralnet R package, with which, by means of different backpropagation algorithms (RPROP+, RPROP−, SLR, SAG, and Backprop), five landslide susceptibility maps (LSMs) were generated for the study area. A landslide inventory updated to 2019 and 10 conditioning factors, mainly topographical, geological, land cover, and hydrological, were considered. The results obtained, which were validated through the AUC-ROC value and statistical parameters of precision, recall, accuracy, and F-Score, showed a good degree of adjustment and an acceptable predictive capacity. The resulting maps showed that the area has mostly sectors of moderate, high, and very high susceptibility, whose landslide occurrence percentages vary between approximately 63% and 80%. In this research, different variants of the backpropagation algorithm were implemented to verify which one gave the best results. With the implementation of additional methodologies and correct zoning, future analyses could be developed, contributing to adequate territorial planning and better disaster risk management in the area.

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Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador

et al. 2022

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“…However, seldom has been the case where truly an approach has been taken to map landslides outside the regions where the models are initially trained on, and also towards actually applying the proposed models in capturing and mapping event-based landslides that has recently occurred. Some recent works at collectively detecting and mapping landslides of different countries have been attempted by (Prakash et al, 2021) and (Ghorbanzadeh et al, 2022), which showcases the power of employing AI at mapping landslides. However, the core of these studies also heavily relies on the availability of quantity and quality data for training an AI model.…”

Section: Introductionmentioning

confidence: 99%

“…The contemporary works of the current literature brings about a critical discussion about the availability and accessibility of comprehensive and adequate data to effectively train models to detect landslides. Both (Prakash et al, 2021) and (Ghorbanzadeh et al, 2022) have used open-source Sentinel-2 imageries for multi-site landslide detection however, considering the fact that the spatial resolution is 10 metres, a lot of small landslides are missed out or not accurately captured (Meena et al, 2022b). The latter attempted to design a benchmark data set for landslide model training using moderate resolution sentinel-2 data and combined it with DEM derived data from ALOS-PALSAR.…”

Section: Introductionmentioning

confidence: 99%

HR-GLDD: A globally distributed dataset using generalized DL for rapid landslide mapping on HR satellite imagery

Meena

Nava²,

Bhuyan³

et al. 2022

Preprint

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Abstract. Multiple landslide events occur often across the world which has the potential to cause significant harm to both human life and property. Although a substantial amount of research has been conducted to address mapping of landslides using Earth Observation (EO) data, several gaps and uncertainties remain when developing models to be operational at the global scale. To address this issue, we present the HR-GLDD, a high resolution (HR) dataset for landslide mapping composed of landslide instances from ten different physiographical regions globally: South and South-East Asia, East Asia, South America, and Central America. The dataset contains five rainfall triggered and five earthquake-triggered multiple landslide events that occurred in varying geomorphological and topographical regions. HR-GLDD is one of the first datasets for landslide detection generated by high-resolution satellite imagery which can be useful for applications in artificial intelligence for landslide segmentation and detection studies. Five state-of-the-art deep learning models were used to test the transferability and robustness of the HR-GLDD. Moreover, two recent landslide events were used for testing the performance and usability of the dataset to comment on the detection of newly occurring significant landslide events. The deep learning models showed similar results for testing the HR-GLDD in individual test sites thereby indicating the robustness of the dataset for such purposes. The HR-GLDD can be accessed open access and it has the potential to calibrate and develop models to produce reliable inventories using high-resolution satellite imagery after the occurrence of new significant landslide events. The HR-GLDD will be updated regularly by integrating data from new landslide events.

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“…By integrating an OBIA-based model and ResU-Net, the proposed model addressed the fuzzy landslide boundaries problem by enhancing and refining the results generated by ResU-Net and possessed higher precision, recall, and F1-score. Besides, Ghorbanzadeh et al [41] created a public landslide dataset for the landslide detection community, denoted as Landslide4Sense, which contains 3799 images fusing optical bands from Sentinel-2 with DEM and slope. Overall, CNN possesses robustness and scalability, and features are automatically extracted through hierarchical structure and convolution, avoiding the involvement of excessive domain knowledge [42].…”

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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Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection

Cited by 15 publications

References 74 publications

Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador

Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador

HR-GLDD: A globally distributed dataset using generalized DL for rapid landslide mapping on HR satellite imagery

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

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