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Application of deep learning in ecological resource research: Theories, methods, and challenges

文献类型：期刊论文

作者Guo, Qinghua4; Jin, Shichao4; Li, Min3; Yang, Qiuli4; Xu, Kexin4; Ju, Yuanzhen1; Zhang, Jing4; Xuan, Jing3; Liu, Jin; Su, Yanjun
刊名SCIENCE CHINA-EARTH SCIENCES
出版日期2020
卷号63期号:10页码:1457-1474
关键词Ecological resources Deep learning Neural network Big data Theory and tools Application and challenge
ISSN号1674-7313
DOI10.1007/s11430-019-9584-9
文献子类Review
英文摘要Ecological 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.
学科主题Geosciences, Multidisciplinary
出版地BEIJING
电子版国际标准刊号1869-1897
WOS关键词CONVOLUTIONAL NEURAL-NETWORK ; SEMANTIC SEGMENTATION ; CLOUD DETECTION ; POINT CLOUDS ; CLASSIFICATION ; IMAGERY ; LIDAR ; IDENTIFICATION ; RECOGNITION ; ALGORITHM
WOS研究方向Geology
语种英语
WOS记录号WOS:000524962500002
出版者SCIENCE PRESS
资助机构Strategic 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]
源URL[http://ir.ibcas.ac.cn/handle/2S10CLM1/21822]  
专题植被与环境变化国家重点实验室
作者单位1.Chinese Acad Sci, Inst Bot, State Key Lab Systemat & Evolutionary Bot, Beijing 100093, Peoples R China
2.Chengdu Univ Technol, State Key Lab Geohazard Prevent & Geoenvironm, Chengdu 610059, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
5.Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：植物研究所

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