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Large-scale automatic extraction of agricultural greenhouses based on high-resolution remote sensing and deep learning technologies

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Abstract

Widely used agricultural greenhouses are critical in the development of facility agriculture because of not only their huge capacity in food and vegetable supplies, but also their environmental and climatic effects. Therefore, it is important to obtain the spatial distribution of agricultural greenhouses for agricultural production, policy making, and even environmental protection. Remote sensing technologies have been widely used in greenhouse extraction mainly in small or local regions, while large-scale and high-resolution (~ 1-m) greenhouse extraction is still lacking. In this study, agricultural greenhouses in an important agricultural province (Shandong, China) are extracted by the combination of high-resolution remote sensing images from Google Earth and deep learning algorithm with high accuracy (94.04% for mean intersection over union over test set). The results demonstrated that the agricultural greenhouses cover an area of 1755.3 km2, accounting for 1.11% of the total province and 2.31% of total cultivated land. The spatial density map of agricultural greenhouses also suggested that the facility agriculture in Shandong has obviously regional aggregation characteristics, which is vulnerable in both environment and economy. The results of this study are useful and meaningful for future agriculture planning and environmental management.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This research is funded by the National Key R&D Program of China (2021YFF0704400), National Natural Science Foundation of China (42371351), the Li Zhengqiang Expert Workstation of Yunnan Province (202205AF150031), and the Yue Qi Young Scholar Project, CUMTB.

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Authors and Affiliations

  1. College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing, 100083, China

    Wei Chen, Jiajia Li, Yameng Xu & Qingpeng Wang

  2. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China

    Dongliang Wang & Xiaohan Liao

  3. School of Information Engineering, Kunming University, Kunming, 650000, China

    Zhenting Chen

Authors
  1. Wei Chen
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  2. Jiajia Li
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  5. Xiaohan Liao
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  6. Qingpeng Wang
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  7. Zhenting Chen
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Contributions

Wei Chen: conceptualization, methodology, investigation, funding acquisition. Jiajia Li: data curation, writing. Dongliang Wang: methodology, investigation. Yameng Xu: validation. Qingpeng Wang: visualization. Zhenting Chen: validation. Xiaohan Liao: data curation.

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Correspondence to Wei Chen.

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Chen, W., Li, J., Wang, D. et al. Large-scale automatic extraction of agricultural greenhouses based on high-resolution remote sensing and deep learning technologies. Environ Sci Pollut Res 30, 106671–106686 (2023). https://doi.org/10.1007/s11356-023-29802-0

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