Marine Equipment Siting Using Machine-Learning-Based Ocean Remote Sensing Data: Current Status and Future Prospects

Dapeng Zhang, Yunsheng Ma, Huiling Zhang () and Yi Zhang
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Dapeng Zhang: Ship and Maritime College, Guangdong Ocean University, Zhanjiang 524088, China
Yunsheng Ma: Ship and Maritime College, Guangdong Ocean University, Zhanjiang 524088, China
Huiling Zhang: College of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, China
Yi Zhang: Ship and Maritime College, Guangdong Ocean University, Zhanjiang 524088, China

Sustainability, 2024, vol. 16, issue 20, 1-26

Abstract: As the global climate changes, there is an increasing focus on the oceans and their protection and exploitation. However, the exploration of the oceans necessitates the construction of marine equipment, and the siting of such equipment has become a significant challenge. With the ongoing development of computers, machine learning using remote sensing data has proven to be an effective solution to this problem. This paper reviews the history of remote sensing technology, introduces the conditions required for site selection through measurement analysis, and uses cluster analysis methods to identify areas such as machine learning as a research hotspot for ocean remote sensing. The paper aims to integrate machine learning into ocean remote sensing. Through the review and discussion of this article, limitations and shortcomings of the current stage of ocean remote sensing are identified, and relevant development proposals are put forward.

Keywords: ocean remote sensing; machine learning; site selection; data requirements; bibliometrics; cluster analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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