Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery

doi:10.1038/s41467-023-37136-1

	Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery
	Sun, Xian 1,2,3; Yin, Dongshuo 1,2,3; Qin, Fei 2; Yu, Hongfeng 1,2,3; Lu, Wanxuan 1,2,3; Yao, Fanglong 1,2,3; He, Qibin 1,2,3; Huang, Xingliang 1,2,3; Yan, Zhiyuan 1,2,3; Wang, Peijin 1,2,3; Deng, Chubo 1,3; Liu, Nayu 1,2,3; Yang, Yiran 1,2,3; Liang, Wei 1; Wang, Ruiping 4; Wang, Cheng 5,6; Yokoya, Naoto 7,8; Haensch, Ronny 9; Fu, Kun 1,2,3
	2023-03-15
发表期刊	NATURE COMMUNICATIONS
卷号	14 期号:1 页码:13
摘要	Dumpsites are hard to locate globally. Here the authors apply deep networks to satellite images to provide an effective and low-cost way to detect dumpsites with the new method saving more than 96.8% of the manual time with a strong sensitivity to dumpsites. With the advancement of global civilisation, monitoring and managing dumpsites have become essential parts of environmental governance in various countries. Dumpsite locations are difficult to obtain in a timely manner by local government agencies and environmental groups. The World Bank shows that governments need to spend massive labour and economic costs to collect illegal dumpsites to implement management. Here we show that applying novel deep convolutional networks to high-resolution satellite images can provide an effective, efficient, and low-cost method to detect dumpsites. In sampled areas of 28 cities around the world, our model detects nearly 1000 dumpsites that appeared around 2021. This approach reduces the investigation time by more than 96.8% compared with the manual method. With this novel and powerful methodology, it is now capable of analysing the relationship between dumpsites and various social attributes on a global scale, temporally and spatially.
DOI	10.1038/s41467-023-37136-1
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2021YFB3900504] ; National Natural Science Foundation of China[61725105] ; National Natural Science Foundation of China[62171436] ; National Major Project on High Resolution Earth Observation System[GFZX0404120405]
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001001760400008
出版者	NATURE PORTFOLIO
引用统计	被引频次：35[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21198
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Sun, Xian; Fu, Kun
作者单位	1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Network Informat Syst Technol NIST, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 5.Xiamen Univ, Sch Informat Sci & Engn, Fujian Key Lab Sensing & Comp Smart Cities, Xiamen 361005, Peoples R China 6.Fujian Collaborat Innovat Ctr Big Data Applicat Go, Fuzhou 350003, Peoples R China 7.RIKEN, RIKEN Ctr Adv Intelligence Project, Tokyo 1030027, Japan 8.Univ Tokyo, Dept Complex Sci & Engn, Tokyo 1138654, Japan 9.German Aerosp Ctr DLR, D-82234 Wessling, Germany
推荐引用方式 GB/T 7714	Sun, Xian,Yin, Dongshuo,Qin, Fei,et al. Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery[J]. NATURE COMMUNICATIONS,2023,14(1):13.
APA	Sun, Xian.,Yin, Dongshuo.,Qin, Fei.,Yu, Hongfeng.,Lu, Wanxuan.,...&Fu, Kun.(2023).Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery.NATURE COMMUNICATIONS,14(1),13.
MLA	Sun, Xian,et al."Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery".NATURE COMMUNICATIONS 14.1(2023):13.