Linwei Du
Nankai University
7 Papers
Linwei Du is an academic researcher from Nankai University. The author has contributed to research in topics: Environmental impact assessment & Work (physics). The author has an hindex of 1, co-authored 1 publications.
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Papers
Analysis of spatial-temporal association and factors influencing environmental pollution incidents in China
Linwei Du,Huizhi Wang,He Xu +2 more
TL;DR: Wang et al. as discussed by the authors used spatial statistical methods and geographically weighted regression (GWR) models to study the temporal and spatial evolution trends of environmental events in China and the spatial correlation characteristics of influencing factors and environmental events.
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Effectiveness of solid waste management policies in Australia: An Exploratory Study
TL;DR: In this paper , the effectiveness of four waste recycling policies was analyzed in Australia using repeated measures analysis of variance method and the results showed that regions with combination policies outperformed in improving solid waste recycling rates.
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Automatic identification of illegal construction and demolition waste landfills: A computer vision approach.
Qiaoqiao Yong,Huanyu Wu,Jiayuan Wang,Run Chen,Bo Yu,Jian Zuo,Linwei Du +6 more
TL;DR: This study proposes a computer vision approach using semantic segmentation of remote sensing imagery to automatically identify construction and demolition waste landfills, achieving 96.30% accuracy and 74.60% IoU, with potential applications in large-scale landfill detection and supervision.
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Transnational recycling of Australian export waste: An exploratory study
TL;DR: The authors analyzes the transnational recycling of waste from the perspective of waste exporting countries and finds that exported waste, assumed to be recycled in the Australian waste statistics, is not fully recycled by waste-importing countries.
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Assessing and predicting the illegal dumping risks in relation to road characteristics.
Linwei Du,Jian Zuo,J. Vanzo,Ruidong Chang,George Zillante +4 more
TL;DR: This study uses geo-information technology and machine learning to assess and predict illegal dumping risks in low-population density areas, improving accuracy by combining dumping locations with road characteristics, and identifying influencing factors and their priorities.
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