Deep learning in environmental remote sensing: Achievements and challenges

• Published in: Remote sensing of environment Vol. 241; p. 111716
• Main Authors: Yuan, Qiangqiang, Shen, Huanfeng, Li, Tongwen, Li, Zhiwei, Li, Shuwen, Jiang, Yun, Xu, Hongzhang, Tan, Weiwei, Yang, Qianqian, Wang, Jiwen, Gao, Jianhao, Zhang, Liangpei
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.05.2020; Elsevier BV
• Subjects: air; Air temperature; artificial intelligence; Atmospheric models; color; Data integration; Deep learning; Earth; environmental factors; Environmental monitoring; Environmental remote sensing; Evapotranspiration; Hydrology; Land cover; Land surface temperature; Learning algorithms; Machine learning; Mapping; Multisensor fusion; Neural network; Neural networks; Ocean color; Parameter retrieval; prediction; Remote sensing; Solar radiation; Surface temperature; vegetation; Deep learning; Environmental remote sensing; Neural network; Parameter retrieval

Various forms of machine learning (ML) methods have historically played a valuable role in environmental remote sensing research. With an increasing amount of “big data” from earth observation and rapid advances in ML, increasing opportunities for novel methods have emerged to aid in earth environme...