Nature Communications (Mar 2023)
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,
- Kun Fu
Affiliations
- Xian Sun
- Aerospace Information Research Institute, Chinese Academy of Sciences
- Dongshuo Yin
- Aerospace Information Research Institute, Chinese Academy of Sciences
- Fei Qin
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences
- Hongfeng Yu
- Aerospace Information Research Institute, Chinese Academy of Sciences
- Wanxuan Lu
- Aerospace Information Research Institute, Chinese Academy of Sciences
- Fanglong Yao
- Aerospace Information Research Institute, Chinese Academy of Sciences
- Qibin He
- Aerospace Information Research Institute, Chinese Academy of Sciences
- Xingliang Huang
- Aerospace Information Research Institute, Chinese Academy of Sciences
- Zhiyuan Yan
- Aerospace Information Research Institute, Chinese Academy of Sciences
- Peijin Wang
- Aerospace Information Research Institute, Chinese Academy of Sciences
- Chubo Deng
- Aerospace Information Research Institute, Chinese Academy of Sciences
- Nayu Liu
- Aerospace Information Research Institute, Chinese Academy of Sciences
- Yiran Yang
- Aerospace Information Research Institute, Chinese Academy of Sciences
- Wei Liang
- Aerospace Information Research Institute, Chinese Academy of Sciences
- Ruiping Wang
- Institute of Computing Technology, Chinese Academy of Sciences
- Cheng Wang
- Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Information Science and Engineering, Xiamen University
- Naoto Yokoya
- RIKEN Center for Advanced Intelligence Project, RIKEN
- Ronny Hänsch
- German Aerospace Center (DLR)
- Kun Fu
- Aerospace Information Research Institute, Chinese Academy of Sciences
- DOI
- https://doi.org/10.1038/s41467-023-37136-1
- Journal volume & issue
-
Vol. 14,
no. 1
pp. 1 – 13
Abstract
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.
