News Release

Deep learning: A new engine for ecological resource research

Peer-Reviewed Publication

Science China Press

Figure 3.

image: Ecological resource areas that are impacted and overwhelmed by deep learning (water part) and the areas need to be solved (mountain part) view more 

Credit: ©Science China Press

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.

Deep learning is a big data driven machine learning method that can automatically extracting complex high-dimensional nonlinear features. Although deep leanring has achieved better performance for big data mining than traditional statistical learning and machine learning algorithms, there are still huge challenges when processing ecological resource data, including multi-source/multi-meta heterogeneity, spatial-temporal coupling, geographic correlation, high dimensional complexity, and low signal-to-noise ratio. A recent study clarified the aforementioned frontier issues.

The related research paper entitled "The Application of Deep Learning in the Field of Ecological Resources Research: Theory, Methods, and Challenges" has been published in "Science in China: Earth Science" . Prof. GUO Qinghua and Ph.D. student JIN Shichao from Institute of Botany, Chinese Academy of Sciences are co-first authors. GUO Qinghua is the corresponding author. Prof. LIU Yu from Peking University and Prof. XU Qiang from Chengdu University of Technology are co-authored research teams.

Deep learning has made significant progress in many fields with the accumulation of data, the improvement of computing power, and the progress of algorithms. This study focuses on the application of deep learning in the field of ecological resources. The main contents include:

    1) An overview of the history, development and basic structure of deep learning (Figure 1). The relationships between ecological resource big data research and deep learning structures represented by convolutional neural networks, recurrent neural networks, and graph neural networks were analyzed (Figure 2)

    2) The main tasks of deep learning, common public data sets, and tools in ecological resources were summarized (Figure 2).

    3) The application of deep learning in plant image classification, crop phenotype, and vegetation mapping were demonstrated. The application ability and potential of deep learning in structured and unstructured ecological data were analyzed.

    4) The challenges and prospects of deep learning in the application of ecological resources were analyzed (Figure 3), including standardization and sharing of data, construction of crowdsourcing collection platform, interpretability of deep neural network, hybrid deep learning with domain knowledge, small sample learning, data fusion, and enrichment and intelligence of applications.

This study explored the relationship between deep learning and ecological resource research. It is of great significance for connecting the technological frontier of computer science with the classical theoretical science in the field of ecological resources. This connection will contribute to the establishment of a new paradigm of theoretical discovery and scientific research in the era of ecological resources big data.


This research was funded by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA19050401) and the National Science Foundation of China (Grant Nos. 31971575 & 41871332)

See the article: Guo, Q., Jin, S., Li, M., Yang, Q., Xu, K., Ju, Y., Zhang, J., Xuan, J., Liu, J., Su, Y., Xu, Q., & Liu, Y. (2020). "Application of deep learning in ecological resource research: Theories, methods, and challenges". Science China Earth Sciences. DOI: 10.1007/s11430-019-9584-9

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