Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models

Prakash, Nikhil; Manconi, Andrea; Loew, Simon

doi:10.3390/rs12030346

Cited by 168 publications

(163 citation statements)

References 69 publications

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“…In addition, we did not discuss the impact of the difference in the number of unlabeled samples, and the comparison process of univariate changes was difficult to achieve due to the constant fine-tuning of iterations and network structure. According to the previous deep learning research [80,81], 3000 extended points are enough to meet the complexity of LSM.…”

Section: Susceptibility In Ssl-dnnmentioning

confidence: 99%

Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China

et al. 2020

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Accurate and timely landslide susceptibility mapping (LSM) is essential to effectively reduce the risk of landslide. In recent years, deep learning has been successfully applied to landslide susceptibility assessment due to the strong ability of fitting. However, in actual applications, the number of labeled samples is usually not sufficient for the training component. In this paper, a deep neural network model based on semi-supervised learning (SSL-DNN) for landslide susceptibility is proposed, which makes full use of a large number of spatial information (unlabeled data) with limited labeled data in the region to train the mode. Taking Jiaohe County in Jilin Province, China as an example, the landslide inventory from 2000 to 2017 was collected and 12 metrological, geographical, and human explanatory factors were compiled. Meanwhile, supervised models such as deep neural network (DNN), support vector machine (SVM), and logistic regression (LR) were implemented for comparison. Then, the landslide susceptibility was plotted and a series of evaluation tools such as class accuracy, predictive rate curves (AUC), and information gain ratio (IGR) were calculated to compare the prediction of models and factors. Experimental results indicate that the proposed SSL-DNN model (AUC = 0.898) outperformed all the comparison models. Therefore, semi-supervised deep learning could be considered as a potential approach for LSM.

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Section: Susceptibility In Ssl-dnnmentioning

confidence: 99%

Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China

et al. 2020

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“…Since the start of 2020, articles have been published using convolutional neural networks (CNNs) to detect patterns in LiDAR (Light Detection and Ranging) data, images in the Google Street View database, video data, UAV data, and NASA's Earth Observation (EO) data for a variety of purposes from detecting pedestrians at night to mapping landslides [19][20][21][22][23]. There have been successful efforts using CNN's to detect buried landmines in ground-penetrating radar data, yet there is a lack of research on using CNN to identify surface mines such as the PFM-1 [24,25].…”

Section: Convolutional Neural Network (Cnn) Overviewmentioning

confidence: 99%

Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines

et al. 2020

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Recent advances in unmanned-aerial-vehicle-(UAV-) based remote sensing utilizing lightweight multispectral and thermal infrared sensors allow for rapid wide-area landmine contamination detection and mapping surveys. We present results of a study focused on developing and testing an automated technique of remote landmine detection and identification of scatterable antipersonnel landmines in wide-area surveys. Our methodology is calibrated for the detection of scatterable plastic landmines which utilize a liquid explosive encapsulated in a polyethylene or plastic body in their design. We base our findings on analysis of multispectral and thermal datasets collected by an automated UAV-survey system featuring scattered PFM-1-type landmines as test objects and present results of an effort to automate landmine detection, relying on supervised learning algorithms using a Faster Regional-Convolutional Neural Network (Faster R-CNN). The RGB visible light Faster R-CNN demo yielded a 99.3% testing accuracy for a partially withheld testing set and 71.5% testing accuracy for a completely withheld testing set. Across multiple test environments, using centimeter scale accurate georeferenced datasets paired with Faster R-CNN, allowed for accurate automated detection of test PFM-1 landmines. This method can be calibrated to other types of scatterable antipersonnel mines in future trials to aid humanitarian demining initiatives. With millions of remnant PFM-1 and similar scatterable plastic mines across post-conflict regions and considerable stockpiles of these landmines posing long-term humanitarian and economic threats to impacted communities, our methodology could considerably aid in efforts to demine impacted regions.

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“…Prakash et al [33] proposed a modified U-Net to complete semantic segmentation of landslides at regional scale from Earth observation (EO) data by using ResNet 34 blocks for feature extraction, then compared this method with traditional machine learning methods. The deep learning method outperformed the pixel-based and object-based machine learning methods.…”

Section: Landslide Analysis Based On Neural Networkmentioning

confidence: 99%

Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM

Cui

Meng

et al. 2020

IJGI

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When a landslide happens, it is important to recognize the hazard-affected bodies surrounding the landslide for the risk assessment and emergency rescue. In order to realize the recognition, the spatial relationship between landslides and other geographic objects such as residence, roads and schools needs to be defined. Comparing with semantic segmentation and instance segmentation that can only recognize the geographic objects separately, image captioning can provide richer semantic information including the spatial relationship among these objects. However, the traditional image captioning methods based on RNNs have two main shortcomings: the errors in the prediction process are often accumulated and the location of attention is not always accurate which would lead to misjudgment of risk. To handle these problems, a landslide image interpretation network based on a semantic gate and a bi-temporal long-short term memory network (SG-BiTLSTM) is proposed in this paper. In the SG-BiTLSTM architecture, a U-Net is employed as an encoder to extract features of the images and generate the mask maps of the landslides and other geographic objects. The decoder of this structure consists of two interactive long-short term memory networks (LSTMs) to describe the spatial relationship among these geographic objects so that to further determine the role of the classified geographic objects for identifying the hazard-affected bodies. The purpose of this research is to judge the hazard-affected bodies of the landslide (i.e., buildings and roads) through the SG-BiTLSTM network to provide geographic information support for emergency service. The remote sensing data was taken by Worldview satellite after the Wenchuan earthquake happened in 2008. The experimental results demonstrate that SG-BiTLSTM network shows remarkable improvements on the recognition of landslide and hazard-affected bodies, compared with the traditional LSTM (the Baseline Model), the BLEU1 of the SG-BiTLSTM is improved by 5.89%, the matching rate between the mask maps and the focus matrix of the attention is improved by 42.81%. In conclusion, the SG-BiTLSTM network can recognize landslides and the hazard-affected bodies simultaneously to provide basic geographic information service for emergency decision-making.

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Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models

Cited by 168 publications

References 69 publications

Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China

Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China

Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines

Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM

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