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

Xian Sun (), Dongshuo Yin, Fei Qin, Hongfeng Yu, Wanxuan Lu, Fanglong Yao, Qibin He, Xingliang Huang, Zhiyuan Yan, Peijin Wang, Chubo Deng, Nayu Liu, Yiran Yang, Wei Liang, Ruiping Wang, Cheng Wang, Naoto Yokoya, Ronny Hänsch and Kun Fu ()
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Xian Sun: Chinese Academy of Sciences
Dongshuo Yin: Chinese Academy of Sciences
Fei Qin: University of Chinese Academy of Sciences
Hongfeng Yu: Chinese Academy of Sciences
Wanxuan Lu: Chinese Academy of Sciences
Fanglong Yao: Chinese Academy of Sciences
Qibin He: Chinese Academy of Sciences
Xingliang Huang: Chinese Academy of Sciences
Zhiyuan Yan: Chinese Academy of Sciences
Peijin Wang: Chinese Academy of Sciences
Chubo Deng: Chinese Academy of Sciences
Nayu Liu: Chinese Academy of Sciences
Yiran Yang: Chinese Academy of Sciences
Wei Liang: Chinese Academy of Sciences
Ruiping Wang: Chinese Academy of Sciences
Cheng Wang: Xiamen University
Naoto Yokoya: RIKEN Center for Advanced Intelligence Project, RIKEN
Ronny Hänsch: German Aerospace Center (DLR)
Kun Fu: Chinese Academy of Sciences

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-37136-1

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