Measuring the similarity between multipolygons using convex hulls and position graphs

Xu, Yongyang; Xie, Zhen; Chen, Zhanlong; Xie, Mingyu

doi:10.1080/13658816.2020.1800016

Cited by 27 publications

(16 citation statements)

References 41 publications

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“…On the other hand, it is more convenient for the criminals running away after them committing some robbery activities. This is also consistent with some previous research, which states that a space with high permeability would increases the crime risk [47][48][49] .…”

Section: Discovery Of Spatial Interaction Patternssupporting

confidence: 93%

Discovering spatial interaction patterns of near repeat crime by spatial association rules mining

Tao

Xie

et al. 2020

Sci Rep

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Urban crime incidents always exhibit a structure of spatio-temporal dependence. Exploration of the spatio-temporal interactions of crime incidents is critical to understanding the occurrence mechanism and spatial transmission characteristics of crime occurrences, therefore facilitating the determination of policing practices. Although previous researches have repeatedly demonstrated that the crime incidents are spatially clustered, the anisotropic characteristics of spatial interaction has not been fully considered and the detailed spatial transmission of crime incidents has rarely been explored. To better understand the spatio-temporal interaction patterns of crime occurrence, this study proposes a new spatial association mining approach to discover significant spatial transmission routes and related high flow regions. First, all near repeat crime pairs are identified based on spatio-temporal proximity. Then, these links between close pairs are simplified by spatial aggregation on spatial grids. Based on that, measures of the spatio-temporal interactions are defined and a spatial association pattern mining approach is developed to discover significant spatial interaction patterns. Finally, the relationship between significant spatial transmission patterns and road network structure is analyzed. The experimental results demonstrate that our approach is able to effectively discover spatial transmission patterns from massive crime incidents data. Our results are expected to provide effective guidance for crime pattern analysis and even crime prevention.

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Section: Discovery Of Spatial Interaction Patternssupporting

confidence: 93%

Discovering spatial interaction patterns of near repeat crime by spatial association rules mining

Tao

Xie

et al. 2020

Sci Rep

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“…Artificial intelligence and other information technologies analyze the distribution characteristics of multisource data through a computer model, based on which they provide an auxiliary decision‐making basis for geological experts. In the future, polygon similarity (Xu, Xie, et al., 2021) can be treated as a loss for the deep learning model to optimize the results.…”

Section: Resultsmentioning

confidence: 99%

Intelligent Recognition of Ore‐Forming Anomalies Based on Multisource Data Fusion: A Case Study of the Daqiao Mining Area, Gansu Province, China

Cai

Chen

et al. 2021

Earth and Space Science

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Novel prediction methods using artificial intelligence have been developed to improve the identification, discovery, and utilization of new types of mineral resources at new depths or using new technologies. However, most artificial intelligence methods require large training data sets that are often unavailable for mineralization prediction models, leading to inaccuracies. To address this issue, we developed a semi‐supervised machine‐learning method to identify metallogenic anomalies using the density‐based spatial clustering of applications with noise method and autoencoder. The outputs of this method show irregularity in distributions inferred from geological, geochemical, and hyperspectral remote sensing data that match known mineralization locations. We focus on the Daqiao mining area of Gansu Province in China to show that the model predictions are highly consistent with known deposits of the Yinmahe and Daqiao gold mines, and two new prospecting areas have been highlighted for further field confirmation. The accuracy of this semi‐supervised learning method was verified by an interdisciplinary intelligent analysis, showing that this method could have wide‐reaching applications for improving regional geological surveys.

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“…where PCm(x) is the phase congruency and is the maximum of PC1(x) and PC2(x); SL(x) is the coupling of Spc(x) and SG(x), the Spc(x) and SG(x) is calculated by PC1(x) and PC2(x). There are many researchers have developed a lot of methods for evaluating the quality of images or vector [6,59], To quantify better the advantages of this method, two evaluation indicators, called PSNR, structural similarity index measure (SSIM) and feature similarity (FSIM), are used. PSNR is defined by mean square error (MSE) and is commonly used as an indicator to assess the quality of processed images.…”

Section: Resultsmentioning

confidence: 99%

Unsupervised Haze Removal for High-Resolution Optical Remote-Sensing Images Based on Improved Generative Adversarial Networks

Hu¹,

Xie

et al. 2020

Remote Sensing

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One major limitation of remote-sensing images is bad weather conditions, such as haze. Haze significantly reduces the accuracy of satellite image interpretation. To solve this problem, this paper proposes a novel unsupervised method to remove haze from high-resolution optical remote-sensing images. The proposed method, based on cycle generative adversarial networks, is called the edge-sharpening cycle-consistent adversarial network (ES-CCGAN). Most importantly, unlike existing methods, this approach does not require prior information; the training data are unsupervised, which mitigates the pressure of preparing the training data set. To enhance the ability to extract ground-object information, the generative network replaces a residual neural network (ResNet) with a dense convolutional network (DenseNet). The edge-sharpening loss function of the deep-learning model is designed to recover clear ground-object edges and obtain more detailed information from hazy images. In the high-frequency information extraction model, this study re-trained the Visual Geometry Group (VGG) network using remote-sensing images. Experimental results reveal that the proposed method can recover different kinds of scenes from hazy images successfully and obtain excellent color consistency. Moreover, the ability of the proposed method to obtain clear edges and rich texture feature information makes it superior to the existing methods.

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Measuring the similarity between multipolygons using convex hulls and position graphs

Cited by 27 publications

References 41 publications

Discovering spatial interaction patterns of near repeat crime by spatial association rules mining

Discovering spatial interaction patterns of near repeat crime by spatial association rules mining

Intelligent Recognition of Ore‐Forming Anomalies Based on Multisource Data Fusion: A Case Study of the Daqiao Mining Area, Gansu Province, China

Unsupervised Haze Removal for High-Resolution Optical Remote-Sensing Images Based on Improved Generative Adversarial Networks

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