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Application of deep learning in ecological resource research: Theories, methods, and challenges
Guo, Qinghua1; Jin, Shichao1; Li, Min2; Yang, Qiuli1; Xu, Kexin1; Ju, Yuanzhen3; Zhang, Jing1; Xuan, Jing2; Liu, Jin; Su, Yanjun; Xu, Qiang3; Liu, Yu4
AbstractEcological resources are an important material foundation for the survival, development, and self-realization of human beings. In-depth and comprehensive research and understanding of ecological resources are beneficial for the sustainable development of human society. Advances in observation technology have improved the ability to acquire long-term, cross-scale, massive, heterogeneous, and multi-source data. Ecological resource research is entering a new era driven by big data. Traditional statistical learning and machine learning algorithms have problems with saturation in dealing with big data. Deep learning is a method for automatically extracting complex high-dimensional nonlinear features, which is increasingly used for scientific and industrial data processing because of its ability to avoid saturation with big data. To promote the application of deep learning in the field of ecological resource research, here, we first introduce the relationship between deep learning theory and research on ecological resources, common tools, and datasets. Second, applications of deep learning in classification and recognition, detection and localization, semantic segmentation, instance segmentation, and graph neural network in typical spatial discrete data are presented through three cases: species classification, crop breeding, and vegetation mapping. Finally, challenges and opportunities for the application of deep learning in ecological resource research in the era of big data are summarized by considering the characteristics of ecological resource data and the development status of deep learning. It is anticipated that the cooperation and training of cross-disciplinary talents may promote the standardization and sharing of ecological resource data, improve the universality and interpretability of algorithms, and enrich applications with the development of hardware.
KeywordEcological resources Deep learning Neural network Big data Theory and tools Application and challenge
Subject AreaGeosciences, Multidisciplinary
Indexed BySCI
WOS Research AreaGeology
WOS IDWOS:000524962500002
Publication PlaceBEIJING
Funding OrganizationStrategic Priority Research Program of Chinese Academy of SciencesChinese Academy of Sciences [XDA19050401] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [31971575, 41871332]
Corresponding Author Emailqguo@ibcas.ac.cn
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Cited Times:16[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Bot, State Key Lab Systemat & Evolutionary Bot, Beijing 100093, Peoples R China
4.Chengdu Univ Technol, State Key Lab Geohazard Prevent & Geoenvironm, Chengdu 610059, Peoples R China
5.Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China
Recommended Citation
GB/T 7714
Guo, Qinghua,Jin, Shichao,Li, Min,et al. Application of deep learning in ecological resource research: Theories, methods, and challenges[J]. SCIENCE CHINA-EARTH SCIENCES,2020,63(10):1457-1474.
APA Guo, Qinghua.,Jin, Shichao.,Li, Min.,Yang, Qiuli.,Xu, Kexin.,...&Liu, Yu.(2020).Application of deep learning in ecological resource research: Theories, methods, and challenges.SCIENCE CHINA-EARTH SCIENCES,63(10),1457-1474.
MLA Guo, Qinghua,et al."Application of deep learning in ecological resource research: Theories, methods, and challenges".SCIENCE CHINA-EARTH SCIENCES 63.10(2020):1457-1474.
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