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

doi:10.1007/s11430-019-9584-9

CORC > 植物研究所 > 中国科学院植物研究所 > 植被与环境变化国家重点实验室

	Application of deep learning in ecological resource research: Theories, methods, and challenges
	Guo, Qinghua 5; Jin, Shichao 5; Li, Min 1; Yang, Qiuli 5; Xu, Kexin 5; Ju, Yuanzhen 2; Zhang, Jing 5; Xuan, Jing 1; Liu, Jin ; Su, Yanjun
刊名	SCIENCE CHINA-EARTH SCIENCES
	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
DOI	10.1007/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
电子版国际标准刊号	1869-1897
出版地	BEIJING
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
出版者	SCIENCE PRESS
WOS记录号	WOS:000524962500002
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]
内容类型	期刊论文
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.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Bot, State Key Lab Systemat & Evolutionary Bot, Beijing 100093, Peoples R China 3.Chengdu Univ Technol, State Key Lab Geohazard Prevent & Geoenvironm, Chengdu 610059, Peoples R China 4.Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China 5.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, 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.