High-resolution Image based Landslides Dataset in Loess Plateau

Hu, Sheng; Qiu, Haijun; WANG, Ninglian; Cui, Yifei; Cao, Mingming

doi:10.3974/geodb.2020.04.08.v1

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The landslide data used in this paper were derived from the Loess Plateau landslide database constructed by Sheng Hu's team [36]. The team carried out landslide unmanned aerial vehicle (UAV) survey work along six typical routes of the Loess Plateau from March 2017 to November 2018, extracted information such as geometric feature parameters and terrain feature parameters of 307 loess landslides using ArcGIS and Global Mapper, and catalogued the landslides to obtain a database of loess plateau landslides based on highresolution images and terrain data.…”

Section: Experimental Datamentioning

confidence: 99%

Landslide Identification Method Based on the FKGRNet Model for Remote Sensing Images

Zhang

Liu

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Currently, researchers commonly use convolutional neural network (CNN) models for landslide remote sensing image recognition. However, with the increase in landslide monitoring data, the available multimodal landslide data contain rich feature information, and existing landslide recognition models have difficulty utilizing such data. A knowledge graph is a linguistic network knowledge base capable of storing and describing various entities and their relationships. A landslide knowledge graph is used to manage multimodal landslide data, and by integrating this graph into a landslide image recognition model, the given multimodal landslide data can be fully utilized for landslide identification. In this paper, we combine knowledge and models, introduce the use of landslide knowledge graphs in landslide identification, and propose a landslide identification method for remote sensing images that fuses knowledge graphs and ResNet (FKGRNet). We take the Loess Plateau of China as the study area and test the effect of the fusion model by comparing the baseline model, the fusion model and other deep learning models. The experimental results show that, first, with ResNet34 as the baseline model, the FKGRNet model achieves 95.08% accuracy in landslide recognition, which is better than that of the baseline model and other deep learning models. Second, the FKGRNet model with different network depths has better landslide recognition accuracy than its corresponding baseline model. Third, the FKGRNet model based on feature splicing outperforms the fused feature classifier in terms of both accuracy and F1-score on the landslide recognition task. Therefore, the FKGRNet model can make fuller use of landslide knowledge to accurately recognize landslides in remote sensing images.

show abstract

Section: Experimental Datamentioning

confidence: 99%

Landslide Identification Method Based on the FKGRNet Model for Remote Sensing Images

Zhang

Liu

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Loess, as an environmentally sustainable building material, finds extensive application in diverse projects within loess regions. In China, loess soils encompass approximately 6.6% of the country's total land area, while globally they constitute approximately 10% of the Earth's land area [1]. With the deep development of Western China (key infrastructure projects on the western seasonal permafrost areas), the problem of freeze-thaw disasters of loess is highlighted [2][3][4], and the freeze-thaw effect seriously affects the construction and operation of loess areas, such as loess-area tunnels in the construction process, due to soil relaxation; poor bearing capacity leads to poor control of tunnel construction quality [5].…”

Section: Introductionmentioning

confidence: 99%

Study of the Applicability of the Vyalov Long-Term Strength Prediction Equation under Freezing and Thawing Conditions

Zhang

et al. 2023

Sustainability

View full text Add to dashboard Cite

In order to determine the appropriate utilization conditions of the Vyalov long-term strength prediction equation, this study selected three soil samples from the loess region. These samples were subjected to varying numbers of freeze–thaw cycles, namely 0, 4, 6, 8, 10, 50, and 100 cycles. Subsequently, post-test soil samples underwent spherical template indenter press-in tests and grain size determinations. The experimental outcomes demonstrated that the Vyalov long-term strength prediction equation accurately reflected the long-term strength variations of frozen loess after 10 freeze–thaw cycles. A further analysis revealed that the stability of the soil samples’ grain composition played a vital role in ensuring the accuracy of the prediction equation. Notably, a highly significant positive correlation was observed between the silt content of the soil samples and the prediction equation parameter β. Moreover, employing the Vyalov long-term strength prediction equation after 10 freeze–thaw cycles yielded prediction results consistent with the freeze–thaw cycles–time analogy method for durations of 10, 20, and 30 years. This study is beneficial for the construction and operation of projects in loess areas.

show abstract

Research on landslide spatial prediction method based on knowledge graph

Bo,

Yang,

Xiao

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

View full text Add to dashboard Cite

Landslide spatial prediction is of great significance for disaster prevention and mitigation. Traditional landslide spatial prediction methods rely on the weights of characteristic factors subjectively set by domain experts, and do not consider the correlation between historical landslide cases. This paper combines the knowledge graph link prediction task with the landslide space prediction task, and proposes a landslide space prediction method based on landslide cases knowledge graph and RGCN (Relational Graph Convolutional Network). First, the conceptual modeling of landslide cases is realized by analyzing the domain knowledge of landslides, and the storage of historical landslide cases data is carried out with the graph database as the carrier; secondly, the feature information of the neighborhood nodes and edges of entities in the knowledge graph is aggregated by RGCN, and the graph structure information in the landslide cases knowledge graph is effectively used; finally, the specific cases of loess landslide are selected as the reasoning object to verify the proposed method. The results demonstrate the effectiveness and accuracy of the method in landslide spatial prediction tasks. At the same time, it also provides a new idea for the pre-disaster early warning and hidden danger investigation of landslide disasters.

show abstract

High-resolution Image based Landslides Dataset in Loess Plateau

Cited by 3 publications

References 0 publications

Landslide Identification Method Based on the FKGRNet Model for Remote Sensing Images

Landslide Identification Method Based on the FKGRNet Model for Remote Sensing Images

Study of the Applicability of the Vyalov Long-Term Strength Prediction Equation under Freezing and Thawing Conditions

Research on landslide spatial prediction method based on knowledge graph

Contact Info

Product

Resources

About