Solid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach

被引:7
作者
Niu, Bowen [1 ]
Feng, Quanlong [1 ]
Yang, Jianyu [1 ]
Chen, Boan [2 ]
Gao, Bingbo [1 ]
Liu, Jiantao [3 ]
Li, Yi [4 ]
Gong, Jianhua [4 ]
机构
[1] China Agr Univ, Coll Land Sci & Technol, Beijing, Peoples R China
[2] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan, Peoples R China
[3] Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan, Shandong, Peoples R China
[4] Chinese Acad Sci, Aerosp Informat Res Inst, Natl Engn Res Ctr Geoinformat, Beijing, Peoples R China
关键词
solid waste; remote sensing; deep learning; feature fusion; BEACH LITTER;
D O I
10.1080/10106049.2022.2164361
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The urbanization worldwide leads to the rapid increase of solid waste, posing a threat to environment and people's wellbeing. However, it is challenging to detect solid waste sites with high accuracy due to complex landscape, and very few studies considered solid waste mapping across multi-cities and in large areas. To tackle this issue, this study proposes a novel deep learning model for solid waste mapping from very high resolution remote sensing imagery. By integrating a multi-scale dilated convolutional neural network (CNN) and a Swin-Transformer, both local and global features are aggregated. Experiments in China, India and Mexico indicate that the proposed model achieves high performance with an average accuracy of 90.62%. The novelty lies in the fusion of CNN and Transformer for solid waste mapping in multi-cities without the need for pixel-wise labelled data. Future work would consider more sophisticated methods such as semantic segmentation for fine-grained solid waste classification.
引用
收藏
页数:17
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