Combining Spectral and Morphometric Properties of Landslides for Separating Individual Landslides Based on Object-Oriented Method

Lin, Qigen; Zou, Zhenhua; Lin, Le; Wang, Ying

doi:10.1007/978-3-319-53498-5_8

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Sun and Liu et al used high spatial resolution GF (high score)-1 remote sensing images to finely classify the construction land in the planned restricted area of nuclear power plants [24]. The object-oriented landslide individual separation method proposed by Lin et al can well solve the problem of rapid risk assessment of landslide hazards [25]. Fathizad In this paper, object-oriented technology is used to extract the surface information of the study area, and the classification accuracy of the image is improved by multi-scale segmentation of the satellite remote sensing image of the study area.…”

Section: Research On Object-oriented Technologymentioning

confidence: 99%

Evaluation Model of Parking Equipment Planning and Design Based on Object-Oriented Technology

Sun

Luo

et al. 2021

Applied Sciences

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Stereo parking equipment has become an important means to solve the problem of parking difficulties, so it is necessary to study the planning of stereo parking equipment. This paper proposes an evaluation model for parking equipment planning and design, and verifies the feasibility of the model through an example. First, obtain the surface information of the planned area through object-oriented technology, and then complete the design layout of the area that can accommodate the most parking spaces according to the plan information map of the study area. Next, calculate the number of parking spaces required for each building in the area, and the number of available parking spaces within the maximum acceptable time for each building. Finally, compare the two to design the number and location of parking equipment. This method can quickly and accurately obtain the ground plane information map of the study area, while ensuring the capacity of parking spaces to meet the needs of users, it also improves the rationality and suitability of the planning and layout of stereo parking equipment, which can effectively guide the planning and construction of urban parking equipment.

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Section: Research On Object-oriented Technologymentioning

confidence: 99%

Evaluation Model of Parking Equipment Planning and Design Based on Object-Oriented Technology

Sun

Luo

et al. 2021

Applied Sciences

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“…Many pixel-based LDM methods have been proposed in recent years, such as logistic regression [26,27], information value model [28], naive Bayes [29,30], support vector machine [31,32], random forests (RF) [33], decision tree [34], and artificial neural network [35,36]. In addition, single or longterm medium and high-resolution optical images are used to extract landslides [37][38][39][40]. Other scholars also combined the characteristics of the study area to add hydrology or nighttime light factors for LDM [41,42].…”

Section: Introductionmentioning

confidence: 99%

Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China

Liu¹,

Chen²,

Niu³

2021

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

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Landslide detection mapping (LDM) is the basis of the field of landslide disaster prevention; however, it has faced certain difficulties. The Three Gorges Reservoir area of the Yangtze River has been one of the most intensively evaluated areas for landslide prevention in the world, due to the high frequency of landslide disasters here. In this study, we constructed an accurate LDM model based on convolutional neural networks (CNNs), residual neural networks (ResNets), and dense convolutional neural networks (DenseNets) that considers 'ZY-3' high spatial resolution (HSR) data and conditioning factors (CFs). In this work, nineteen factors based on remote sensing (RS) images, topographical and geological data associated with historical landslide locations were randomly divided into training (70% of total) and testing (30%) datasets. The experimental results show that the accuracy (ACC) of these three LDM models is above 0.95, indicating that the deep neural networks (DNNs) aimed at landslide detection performed well. Furthermore, DenseNet with RS images and CFs can accurately detect landslides. Specifically, DenseNet with RS images and CFs outperforms the other five models by considering the accuracy evaluation indexes, which exhibited Kappa coefficient improvements of 0.01-0.04 and ACC improvements of 0.02%-0.3%. Among all the factors, elevation factor has a high importance of 0.727, which is the most important factors found in this landslide model construction experiment.

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Introduction: Landslide Field Recognition and Identification: Remote Sensing Techniques and Field Techniques

Auflič

2017

Advancing Culture of Living With Landslides

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Combining Spectral and Morphometric Properties of Landslides for Separating Individual Landslides Based on Object-Oriented Method

Cited by 4 publications

References 12 publications

Evaluation Model of Parking Equipment Planning and Design Based on Object-Oriented Technology

Evaluation Model of Parking Equipment Planning and Design Based on Object-Oriented Technology

Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China

Introduction: Landslide Field Recognition and Identification: Remote Sensing Techniques and Field Techniques

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