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Dr. Wang Weilin
School of Resource and Environmental Sciences, Wuhan University

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Research Keywords & Expertise

0 PM2.5
0 ConvLSTM
0 air pollutant
0 spatial-temporal correlation
0 long-term prediction

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Journal article
Published: 17 August 2021 in Automation in Construction
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Most existing indoor interior segmentation methods typically focus on planar structures rather than curved structures. Random sample consensus-based methods perform curved surface segmentation via regularity model fitting but suffer from spurious model generation when noise and outliers are present due to the uncertainty in the random sampling. This paper formulates indoor interior segmentation by fitting representative models and matching each primary cell with corresponding model simultaneously. The models are fitted by a combination of points and supervoxels with adaptive resolutions instead of just points, guaranteeing the correctness of sampling on the same surface and avoiding spurious models. Cell-to-model matching is achieved by iterative refinement/clustering under the global energy optimization method, which ensures optimal overall segmentations. Experimental tests demonstrate the effectiveness of our method in dealing with both planar and nonplanar surfaces, resulting in performance metrics of approximately 0.75 for the structure F1-score and over 0.9 for edge precision and recall.

ACS Style

Fei Su; Haihong Zhu; Lin Li; Gang Zhou; Wei Rong; Xinkai Zuo; Wende Li; Xinmei Wu; Weilin Wang; Fan Yang; Huanjun Hu; Shen Ying. Indoor interior segmentation with curved surfaces via global energy optimization. Automation in Construction 2021, 131, 103886 .

AMA Style

Fei Su, Haihong Zhu, Lin Li, Gang Zhou, Wei Rong, Xinkai Zuo, Wende Li, Xinmei Wu, Weilin Wang, Fan Yang, Huanjun Hu, Shen Ying. Indoor interior segmentation with curved surfaces via global energy optimization. Automation in Construction. 2021; 131 ():103886.

Chicago/Turabian Style

Fei Su; Haihong Zhu; Lin Li; Gang Zhou; Wei Rong; Xinkai Zuo; Wende Li; Xinmei Wu; Weilin Wang; Fan Yang; Huanjun Hu; Shen Ying. 2021. "Indoor interior segmentation with curved surfaces via global energy optimization." Automation in Construction 131, no. : 103886.

Journal article
Published: 27 March 2021 in Remote Sensing
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Deep learning provides a promising approach for air pollution prediction. The existing deep learning-based predicted models generally consider either the temporal correlations of air quality monitoring stations or the nonlinear relationship between the PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) concentrations and explanatory variables. Spatial correlation has not been effectively incorporated into prediction models, therefore exhibiting poor performance in PM2.5 prediction tasks. Additionally, determining the manner by which to expand longer-term prediction tasks is still challenging. In this paper, to allow for spatiotemporal correlations, a spatiotemporal convolutional recursive long short-term memory (CR-LSTM) neural network model is proposed for predicting the PM2.5 concentrations in long-term prediction tasks by combining a convolutional long short-term memory (ConvLSTM) neural network and a recursive strategy. Herein, the ConvLSTM network was used to capture the complex spatiotemporal correlations and to predict the future PM2.5 concentrations; the recursive strategy was used for expanding the long-term prediction tasks. The CR-LSTM model was used to realize the prediction of the future 24 h of PM2.5 concentrations for 12 air quality monitoring stations in Beijing by configuring both the appropriate time lag derived from the temporal correlations and the spatial neighborhood, including the hourly historical PM2.5 concentrations, the daily mean meteorological data, and the annual nighttime light and normalized difference vegetation index (NDVI). The results showed that the proposed CR-LSTM model achieved better performance (coefficient of determination (R2) = 0.74; root mean square error (RMSE) = 18.96 μg/m3) than other common models, such as multiple linear regression (MLR), support vector regression (SVR), the conventional LSTM model, the LSTM extended (LSTME) model, and the temporal sliding LSTM extended (TS-LSTME) model. The proposed CR-LSTM model, implementing a combination of geographical rules, recursive strategy, and deep learning, shows improved performance in longer-term prediction tasks.

ACS Style

Weilin Wang; Wenjing Mao; Xueli Tong; Gang Xu. A Novel Recursive Model Based on a Convolutional Long Short-Term Memory Neural Network for Air Pollution Prediction. Remote Sensing 2021, 13, 1284 .

AMA Style

Weilin Wang, Wenjing Mao, Xueli Tong, Gang Xu. A Novel Recursive Model Based on a Convolutional Long Short-Term Memory Neural Network for Air Pollution Prediction. Remote Sensing. 2021; 13 (7):1284.

Chicago/Turabian Style

Weilin Wang; Wenjing Mao; Xueli Tong; Gang Xu. 2021. "A Novel Recursive Model Based on a Convolutional Long Short-Term Memory Neural Network for Air Pollution Prediction." Remote Sensing 13, no. 7: 1284.

Journal article
Published: 26 November 2020 in Applied Sciences
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Owing to map scale reduction and other cartographic generalization operations, spatial conflicts may occur between buildings and other features in automatic cartographic generalization. Displacement is an effective map generalization operation to resolve these spatial conflicts to guarantee map clarity and legibility. In this paper, a novel building displacement method based on multipopulation genetic algorithm (BDMPGA) is proposed to resolve spatial conflicts. This approach introduces multiple populations with different control parameters for simultaneous search optimization and adopts an immigration operation to connect different populations to realize coevolution. The optimal individuals of each population are selected and preserved in the elite population through manual selection operation to prevent the optimal individuals from being destroyed and lost in the evolutionary process. Meanwhile, the least preserving generation of the optimal individuals is used as the termination basis. To validate the proposed method, urban building data with a scale of 1:10,000 from Shenzhen, China are used. The experimental results indicate that the method proposed in this paper can effectively resolve spatial conflicts to obtain better results.

ACS Style

Wende Li; Tinghua Ai; Yilang Shen; Wei Yang; Weilin Wang. A Novel Method for Building Displacement Based on Multipopulation Genetic Algorithm. Applied Sciences 2020, 10, 8441 .

AMA Style

Wende Li, Tinghua Ai, Yilang Shen, Wei Yang, Weilin Wang. A Novel Method for Building Displacement Based on Multipopulation Genetic Algorithm. Applied Sciences. 2020; 10 (23):8441.

Chicago/Turabian Style

Wende Li; Tinghua Ai; Yilang Shen; Wei Yang; Weilin Wang. 2020. "A Novel Method for Building Displacement Based on Multipopulation Genetic Algorithm." Applied Sciences 10, no. 23: 8441.

Journal article
Published: 27 October 2020 in Sustainable Cities and Society
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Land-use optimization model provides an effective means of finding solutions to mitigate ecological impacts resulting from land use and land cover changes (LUCCs). However, current land-use optimization models usually underestimate the control/ effectiveness of ecological indicators in the model's operation process. How to incorporate ecological indicators into the land-use simulation to optimize multiple land-use patterns is scarce and worth discussing. In our study, we proposed a Future Land use Optimization model for Ecological protection (FLOE) by integrating a cellular automata (CA) model, ant colony optimization (ACO) algorithm, and ecological protection for optimizing land-use patterns from an ecological priority perspective. Firstly, we discuss the coupling pattern in incorporating ecological indicators into models to support the use of models for design and verification in large-scale land-use optimization. Secondly, the proposed FLOE model improves the effectiveness of ecological indicators in the land-use optimization process and better meets the predetermined optimization objectives in a dynamic feedback mechanism. The LUCCs of the Yangtze River Economic Belt (YREB) during 2010–2015 were selected to validate the applicability of the proposed FLOE model. The validation results show that compared to actual LUCCs, the proposed model can significantly reduce ecosystem function loss. Moreover, the proposed model was also applied to the land use optimization from 2010 to 2030 in YREB. The optimization results show a 31.23 % reduction in the total ecosystem function loss than land-use simulation without ecological optimization. The study is expected to provide a reference for land use optimization modelling with ecological conservation in methodology and offers important implications for the formulation and management of large-scale spatial planning.

ACS Style

Weilin Wang; Limin Jiao; QiQi Jia; Jiafeng Liu; Wenjing Mao; Zhibang Xu; Wende Li. Land use optimization modelling with ecological priority perspective for large-scale spatial planning. Sustainable Cities and Society 2020, 65, 102575 .

AMA Style

Weilin Wang, Limin Jiao, QiQi Jia, Jiafeng Liu, Wenjing Mao, Zhibang Xu, Wende Li. Land use optimization modelling with ecological priority perspective for large-scale spatial planning. Sustainable Cities and Society. 2020; 65 ():102575.

Chicago/Turabian Style

Weilin Wang; Limin Jiao; QiQi Jia; Jiafeng Liu; Wenjing Mao; Zhibang Xu; Wende Li. 2020. "Land use optimization modelling with ecological priority perspective for large-scale spatial planning." Sustainable Cities and Society 65, no. : 102575.

Journal article
Published: 12 June 2020 in Sustainable Cities and Society
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Urban growth boundaries (UGBs) have viewed as an efficient tool to control disorderly urban expansion and promote urban sustainable development. The existing methods related to UGBs delineation have also focused mainly on improving the efficiency and applicability of the methods. However, it is not enough to emphasize simply the superiority of the methods, especially in China. UGBs delineation not only considers regional heterogeneous resource endowments but also further coordinates pre-existing land-use plans and the conflicts with one another. The study presents a novel framework for delineating UGBs from carrying-capacity-oriented perspective whose contributions are twofold. Firstly, the level of carrying capacity on resource and environment for each sub-region was evaluated by carrying capacity analysis. A modified mechanism was used to coordinate the inter-regional trade-off between urban growth and carrying capacity on resource and environment and quantify as the constraints of the algorithm. The suitability evaluations of multiple land-use type, including urban lands, agricultural lands, and ecological lands, are viewed as the optimization objectives of the algorithm together with urban landscape compactness. Second, we develop a comprehensive method coupling carrying capacity analysis, land-use suitability evaluation, and modified ant colony optimization algorithm. It can not only coordinate the conflicts with the land-use plans but also ensure the sustainable development for each sub-region. The dilation and erosion algorithm generates optimal UGBs. The proposed framework was applied to UGBs delineation in Wuhan, a rapidly urbanizing city in China. Multi-scenarios UGBs were delineated by the proposed framework. The results demonstrated carrying-capacity-oriented UGBs delineation perspective is more practical and closer to reality than the results of traditional methods. The conceptual and methodological advancement of this study is applicable to other cities to assist UGBs delineation.

ACS Style

Weilin Wang; Limin Jiao; Weina Zhang; QiQi Jia; Fei Su; Gang Xu; Shifa Ma. Delineating urban growth boundaries under multi-objective and constraints. Sustainable Cities and Society 2020, 61, 102279 .

AMA Style

Weilin Wang, Limin Jiao, Weina Zhang, QiQi Jia, Fei Su, Gang Xu, Shifa Ma. Delineating urban growth boundaries under multi-objective and constraints. Sustainable Cities and Society. 2020; 61 ():102279.

Chicago/Turabian Style

Weilin Wang; Limin Jiao; Weina Zhang; QiQi Jia; Fei Su; Gang Xu; Shifa Ma. 2020. "Delineating urban growth boundaries under multi-objective and constraints." Sustainable Cities and Society 61, no. : 102279.

Journal article
Published: 15 April 2020 in Habitat International
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In the context of urbanization and sustainable development, efficient urban land use is essential, especially in China, the world's most populous country. Within this context, the law of urban scaling reveals the nonlinear scale relationship between urban indicators and urban population, which can be applied to adjust the bias of the raw or the per capita indices used in the measurement of local urban performance at different scales. However, the manner in which the urban scaling law applies to China and how it can be used to assess urban land use efficiency (ULUE) is still unclear. In this study, we employ scale-adjusted metropolitan indicators (SAMIs) to assess ULUE in Chinese cities. We first considered the urban population to calculate the land input performance (LIP) and land output performance (LOP), then we quantify ULUE and identify four related patterns. We further investigate the temporal and spatial variations of ULUE and explore the characteristics and policy implications of ULUE values. Results from our study indicate that ULUE assessments from the perspective of the urban scaling law can effectively correct the bias caused by the urban size, thereby allowing for an objective understanding of performance of cities in different sizes. From 2012 to 2016, ULUE of Chinese cities showed a steadily rising trend. The ULUE patterns of most cities remained unchanged and showed significant “path dependence.” However, the disparity in ULUE between regions is widening. Specifically, the cities in the south are better than those in the north, and the cities in the northeast have significantly deteriorated ULUEs. Some cities showed a high ULUE, especially Shenzhen, Hangzhou, and Wuhan. Spatial autocorrelation analysis suggests that geographically neighboring cities are likely to perform similarly regarding ULUE. In terms of policy implications, our work can provide a clear direction for development of cities in urban size and urban efficiency.

ACS Style

Limin Jiao; Zhibang Xu; Gang Xu; Rui Zhao; Jiafeng Liu; Weilin Wang. Assessment of urban land use efficiency in China: A perspective of scaling law. Habitat International 2020, 99, 102172 .

AMA Style

Limin Jiao, Zhibang Xu, Gang Xu, Rui Zhao, Jiafeng Liu, Weilin Wang. Assessment of urban land use efficiency in China: A perspective of scaling law. Habitat International. 2020; 99 ():102172.

Chicago/Turabian Style

Limin Jiao; Zhibang Xu; Gang Xu; Rui Zhao; Jiafeng Liu; Weilin Wang. 2020. "Assessment of urban land use efficiency in China: A perspective of scaling law." Habitat International 99, no. : 102172.