U-Net: A Smart Application with Multidimensional Attention Network for Remote Sensing Images

Wang, Yao; Kong, Jiayuan; Zhang, Hesheng

doi:10.1155/2022/1603273

Cited by 10 publications

(7 citation statements)

References 26 publications

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“…Through collecting and sorting the rainfall data of the study area in the past 20 years, the Kriging interpolation is used to generate the thematic map of the annual rainfall in the study area (refer to Figure 3(f) for details). Furthermore, vegetation coverage is an impact factor of the landslide, and the thematic map of the vegetation coverage in the study area is obtained through using the ENVI 5.3 software [ 21 ]. In this study, the vegetation coverage is divided into five different categories (as shown in Figure 3(g) ).…”

Section: Methodsmentioning

confidence: 99%

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A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area

Tao

2022

Computational Intelligence and Neuroscience

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The purpose of this study is to compare the results of the frequency ratio (FR) model with the weight of evidence (WOE) and the logical regression (LR) methods when applied to the landslide susceptibility evaluation in coal mining subsidence areas. Key geological disaster prevention and control areas are taken as the research areas. Field investigation is carried out according to the recorded landslide disaster points in the past five years, and 86 landslide disaster points are determined from the remote sensing satellite images. Furthermore, 12 factors affecting the occurrence of landslide are selected as landslide sensitivity evaluation factors. Among them, slope degree, curvature, elevation, and slope aspect are derived using the digital elevation model (DEM) through 30 m × 30 m resolution. The DEM datasets are derived from the geospatial data cloud, lithology datasets are derived from the geological lithology maps, and land use type map is derived from the current situation of national land use. The distances between roads and coal mining subsidence areas are calculated according to field investigation and remote sensing image interpretation results. In addition, the evaluation model includes an annual rainfall distribution map. Finally, the accuracy of three models is compared by ROC curve analysis. The elevation results demonstrate that the frequency ratio-logic regression (FR-LR) model takes the maximum accurateness of 0.913, subsequent to the FR model and the frequency ratio-weight of evidence (FR-WOE) model, respectively. Thus, using LR method based on the FR model has guiding significance for predicting the landslide sensitivity in coal mining. This reduces probable risks and disasters that affect human health. Subsequently, this ensures higher safety from the healthcare perspective in the mining fields.

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Section: Methodsmentioning

confidence: 99%

“…Furthermore, vegetation coverage is an impact factor of the landslide, and the thematic map of the vegetation coverage in the study area is obtained through using the ENVI 5.3 software [ 21 ]. In this study, the vegetation coverage is divided into five different categories (as shown in Figure 3(g) ).…”

Section: Methodsmentioning

confidence: 99%

A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area

Tao

2022

Computational Intelligence and Neuroscience

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“…Methods. We implemented three different machine learning algorithms, that is, CNN, RNN, and U-net [33], where the training happens on a cloud and prediction at the edge. e U-net method is implemented along with the attention mechanism.…”

Section: Results Of the Machine Learning And Aggregationmentioning

confidence: 99%

Surface Movement and Deformation Law Caused by Different Coal Pillar Stagger Distances in Strip Filling IoT-Enabled Sustainable Mining

Zhu

Zhang

Kong

et al. 2022

Mobile Information Systems

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Most of the mines in the middle and lower reaches of the Yellow River Basin in China have the occurrence characteristics of multiple coal seams, and the mining is easy to cause damage to the ecological and human settlement environments. Mine monitoring methods, using Internet of Things (IoT) and machine learning, pursue to develop a suitable atmosphere to avoid similar damages and mine closure with supreme efficacy. Accurate monitoring methods reduce these damages and optimize mining. Strip filling mining is characterized by high efficiency, reliability, and low cost, which provides a good technical support for mining damage control. However, how to reasonably layout the working face under multicoal seam conditions and the law of surface movement under different coal pillar stagger distance need to be studied. Based on the geological and mining conditions of a mine in Shandong Province, through numerical simulation and theoretical analysis, this article studies the influence of coal pillar stagger distance on surface movement of multicoal seam strip filling mining. The study reveals mechanism for surface cooperative deformation under different coal pillar stagger distances. Furthermore, an IoT, cloud computing, and data aggregation based architecture is offered in order to support the development of a digital and sustainable mining platform. The results show that the surface subsidence is positively correlated with coal pillar stagger coefficient and mining layers. When the stagger coefficient s ≥ 0.75, then the surface subsidence and horizontal movement are less affected by the change of coal pillar stagger coefficient. The relationship between surface subsidence ratio and coal pillar stagger coefficient, in multicoal seam strip filling mining, is a power function. The subsidence ratio observed was 0.21–0.32, and the horizontal movement coefficient is about 0.09. The fitting empirical formula of surface subsidence ratio and coal pillar stagger coefficient, in this particular mining area, is established through various machine learning methods. Under the mining conditions of gently inclined coal seam, the surface subsidence can be slowed down to a certain extent by the arrangement of working face along incline direction. Through plausible assumption, our experimental outcomes demonstrate that the proposed prototype can improve the prediction accuracy and model execution times, from 14.2% to 18.9%, through the data aggregation method.

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“…Moreover, the F1 score is the harmonic average of recall rate and precisions (accuracy). Finally, the IoU is the crossing of pixels labelled as building in the ground truths and anticipated outcomes and subsequently divided by the union of pixels labelled as building in the ground truths and forecasted outcomes [8].…”

Section: Discussion and Model Accuracymentioning

confidence: 99%

“…is should be noted that, due to (i) no availability of duplicate entries and (ii) small size of the dataset, we do not use any aggregation technique in this work. Largely, the well-known Euclidean distance equation is used to identify whether two particular collected data points (through sensors) belong to either the same region or two different regions, which is used for data aggregation purpose [8,9]. e processed data is then moved to the cloud for long-term storage.…”

Section: Evaluation Of Debris Flow Susceptibility Based On Random For...mentioning

confidence: 99%

Risk Assessment of Debris Flow in Huyugou River Basin Based on Machine Learning and Mass Flow

2022

Mobile Information Systems

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The Huyugou river basin is a typical debris flow river basin in the Shanxi Province, which has great harm after the outbreak and seriously affects the safety of people’s lives and property. Therefore, it is urgent to carry out debris flow risk assessment. In this paper, a machine learning algorithm is implemented to assess the disaster susceptibility of each branch gully in a river basin of the Huyugou. Furthermore, its high-susceptibility branch gully and main gully were selected as the starting points of debris flow simulation for numerical simulation. The machine learning algorithm is implemented in a cloud-edge platform to minimize the model training and prediction times. Under the simulated rainfall conditions of major debris flow disasters, e.g., the one that occurred in 1996, the accuracy rate reached 84%. The results show that the debris flow susceptibility of each branch gully in the study area is mainly affected by the peak flow rate of the river basin, the length of the main gully, and the relative height difference of the river basin. The total risk area of debris flow is 1.91 × 105 m2, and the high-risk area accounts for 52.18% of the total area. It is mainly located in the upper part of the main gully accumulation area and the confluence of each channel and the main gully. The middle-risk area accounts for 36.14% of the total area, and the low-risk area accounts for less. We also observed significant reduction, from 34.68% to 36.98%, in the training and prediction times of the machine learning models when implemented over the proposed edge-cloud framework. The reappearance of debris flow in the study area is relatively accurate, which provides a certain scientific basis for the risk assessment of debris flow in the future.

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U-Net: A Smart Application with Multidimensional Attention Network for Remote Sensing Images

Cited by 10 publications

References 26 publications

A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area

A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area

Surface Movement and Deformation Law Caused by Different Coal Pillar Stagger Distances in Strip Filling IoT-Enabled Sustainable Mining

Risk Assessment of Debris Flow in Huyugou River Basin Based on Machine Learning and Mass Flow

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