Welcome to Francis Academic Press

Academic Journal of Engineering and Technology Science, 2022, 5(8); doi: 10.25236/AJETS.2022.050808.

Intelligent identification technology of river and lake "four chaos" based on satellite remote sensing data

Author(s)

Qianru Wu, Yaduo Han, Yuxuan Chen, Xinru Li

Corresponding Author:
Qianru Wu
Affiliation(s)

College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, Henan, China

Abstract

This study investigated and collected evidence on the "four chaos" problems of rivers and lakes through cutting-edge information technology, and promoted regulatory upgrading with technological progress to ensure that relevant departments comprehensively strengthened supervision of rivers and lakes in the region. The use of remote sensing information technology to achieve large-scale periodic macro monitoring of the "four chaos" of rivers and lakes, provide timely and reliable technical support for the supervision of the "four chaos" of rivers and lakes, and help the realization of the beautiful ecological vision of "clear water and green mountains"; With the macro and periodicity characteristics of remote sensing monitoring, problems such as incomplete supervision scope, inaccurate information acquisition, untimely acquisition and difficult problem summary and statistics can be solved. On the premise of ensuring accuracy and timeliness, human and material costs can be greatly saved and good economic benefits can be achieved.

Keywords

Automation, Rivers and lakes in chaos, Intelligent identification

Cite This Paper

Qianru Wu, Yaduo Han, Yuxuan Chen, Xinru Li. Intelligent identification technology of river and lake "four chaos" based on satellite remote sensing data. Academic Journal of Engineering and Technology Science (2022) Vol. 5, Issue 8: 40-45. https://doi.org/10.25236/AJETS.2022.050808.

References

[1] Zhang Y. A new algorithm for object detection in remote sensing images based on deep learning [J]. North China Water Resources, (2021) 23-084-03.

[2] Yao Liang, Gao Lei. Water resources development research, 2020, 20(11): 36-40.]

[3] Chen Yuliang, Dong Shaojiang, Sun Shizheng, Yan Kaibo. Improved YOLOv5 algorithm for low-light underwater biological target detection [J/OL]. Journal of Beijing University of Aeronautics and Astronautics: 1-13[2022-07-14]. DOI: 10.13700/ j.B.1001-5965.2022.0322.

[4] Yang Chaochen, Chen Jiayue, Xing Ke, Liu Mengni, Gao Tao. Research on Small Target Detection Algorithm Based on Improved DSSD [J]. Computer Technology and Development, 2021, 32(06): 63-67.

[5] Liu Xinchao, Yan Ying, Gan Haiyun. Unmanned dangerous traffic scene small target detection algorithm research [J]. Science and technology, 2021, 5 (10): 38 and 43. DOI: 10.13774 / j.carol carroll nki KJTB. 2021.10.007.