The construction of personalized virtual landslide disaster environments based on knowledge graphs and deep neural networks

Zhang, Yunhao; Zhu, Jun; Zhu, Qing; Xie, Yakun; Li, Weilian; Fu, Lin; Zhang, Junxiao; Tan, Jianmei

doi:10.1080/17538947.2020.1773950

Cited by 33 publications

(26 citation statements)

References 39 publications

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“…Landslides are a common natural disaster caused by external stimuli such as rainfall, earthquakes, and human activities (Andersson-Sköld et al 2013 ; Dai et al 2002 ; Ju et al 2020 ), resulting in an expansion of shear stress or a decline in soil shear resistance, which easily occurs in mountainous areas (Arnone et al 2011 ; Dai et al 2002 ). It has the characteristics of rapidness, wide range and great destruction (Zhang et al 2020 ). Due to various factors such as climate change, the rapid increase in landslides has become a severe constraint on urbanization and economic development (Haque et al 2019 ).…”

Section: Simulation Training Systems Based On Different Phases Of Eme...mentioning

confidence: 99%

“…Zhang et al ( 2020 ) proposed a method for constructing personalized virtual landslide disaster environments based on knowledge graphs and deep neural networks. Among them, the knowledge graph is used to clarify complex domain knowledge and relationships, and the deep neural network can mine the features and semantic information contained in the knowledge graph.…”

Section: Simulation Training Systems Based On Different Phases Of Eme...mentioning

confidence: 99%

“…Therefore, the simulation training system based on visualization technology has become an important training tool for emergency drills of severe natural disasters. Scholars at home and abroad have carried out comprehensive research on various simulation training systems to address natural disasters (Gong et al 2015 ; Schluse et al 2015 ; Takeuchi 2005 ; Yu et al 2016 ; Zhang et al 2020 , 2019 ). To the best of our knowledge, some literature has made considerable effort to review the major progress in the study of visualization technology for security.…”

Section: Introductionmentioning

confidence: 99%

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Review on visualization technology in simulation training system for major natural disasters

Sun

Cao

et al. 2022

Nat Hazards

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Section: Simulation Training Systems Based On Different Phases Of Eme...mentioning

confidence: 99%

Section: Simulation Training Systems Based On Different Phases Of Eme...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Review on visualization technology in simulation training system for major natural disasters

Sun

Cao

et al. 2022

Nat Hazards

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“…With the development of DCNNs in recent years, many algorithms have been proposed for processing remote sensing images [25][26][27][28][29][30][31][32]. The fully convolutional network [33] (FCN) replaces the fully connected layers with convolutional layers, making it possible for large-scale dense prediction.…”

Section: Related Workmentioning

confidence: 99%

Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction

Liao

Han

et al. 2021

Remote Sensing

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Most of the existing approaches to the extraction of buildings from high-resolution orthoimages consider the problem as semantic segmentation, which extracts a pixel-wise mask for buildings and trains end-to-end with manually labeled building maps. However, as buildings are highly structured, such a strategy suffers several problems, such as blurred boundaries and the adhesion to close objects. To alleviate the above problems, we proposed a new strategy that also considers the contours of the buildings. Both the contours and structures of the buildings are jointly learned in the same network. The contours are learnable because the boundary of the mask labels of buildings implicitly represents the contours of buildings. We utilized the building contour information embedded in the labels to optimize the representation of building boundaries, then combined the contour information with multi-scale semantic features to enhance the robustness to image spatial resolution. The experimental results showed that the proposed method achieved 91.64%, 81.34%, and 74.51% intersection over union (IoU) on the WHU, Aerial, and Massachusetts building datasets, and outperformed the state-of-the-art (SOTA) methods. It significantly improved the accuracy of building boundaries, especially for the edges of adjacent buildings. The code is made publicly available.

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“…Machine learning technology has developed rapidly in recent years, especially with the proposal of the Fully Convolutional Network (FCN) [13], which is a milestone in the field of image processing research and has achieved good results for efficient image segmentation. There have been many deep-learning-based studies related to remote sensing segmentation recently [2,[14][15][16][17][18][19][20][21][22][23][24][25][26]. For the most relevant problems in road extraction research , we briefly review related works, including refined road boundary extraction and continuous road regional recognition.…”

Section: Related Workmentioning

confidence: 99%

Cascaded Residual Attention Enhanced Road Extraction from Remote Sensing Images

Liao

Ding

et al. 2021

IJGI

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Efficient and accurate road extraction from remote sensing imagery is important for applications related to navigation and Geographic Information System updating. Existing data-driven methods based on semantic segmentation recognize roads from images pixel by pixel, which generally uses only local spatial information and causes issues of discontinuous extraction and jagged boundary recognition. To address these problems, we propose a cascaded attention-enhanced architecture to extract boundary-refined roads from remote sensing images. Our proposed architecture uses spatial attention residual blocks on multi-scale features to capture long-distance relations and introduce channel attention layers to optimize the multi-scale features fusion. Furthermore, a lightweight encoder-decoder network is connected to adaptively optimize the boundaries of the extracted roads. Our experiments showed that the proposed method outperformed existing methods and achieved state-of-the-art results on the Massachusetts dataset. In addition, our method achieved competitive results on more recent benchmark datasets, e.g., the DeepGlobe and the Huawei Cloud road extraction challenge.

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The construction of personalized virtual landslide disaster environments based on knowledge graphs and deep neural networks

Cited by 33 publications

References 39 publications

Review on visualization technology in simulation training system for major natural disasters

Review on visualization technology in simulation training system for major natural disasters

Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction

Cascaded Residual Attention Enhanced Road Extraction from Remote Sensing Images

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