Mapping the distribution and dynamics of coastal aquaculture ponds using Landsat time series data based on U²-Net deep learning model

ABSTRACT

The extraction of coastal aquaculture ponds from remote sensing images is affected by the effect of ‘same object with different spectrums’ and ‘different objects with same spectrums’. The U²-Net deep learning network is able to capture more contextual information from different scales to solve this problem and is suitable for salient object detection. This study proposed a remote sensing information extraction model of coastal ponds based on U²-Net deep learning network, completed the remote sensing information extraction of coastal aquaculture ponds in Zhoushan Archipelago from 1984 to 2022, analyzed the spatiotemporal evolution of coastal aquaculture ponds in Zhoushan Archipelago. The results showed that the developed model was more accurate with a precision rate of 96.12%, a recall rate of 95.43%, and an F1-measure of 0.96. During the study period, the area of the aquaculture ponds in the Zhoushan Archipelago demonstrated an increasing trend, expanding from 471.21 hm² in 1984–3668.55 hm² in 2022. In addition, over half of the aquaculture ponds had been active for more than 10 years. The method developed in this study is capable of rapidly and accurately mapping coastal aquaculture ponds, and thus is significant for the management of marine resources and promoting sustainable development.

KEYWORDS:

• Coastal aquaculture ponds
• U²-Net
• deep learning model
• spatiotemporal patterns
• accuracy assessment

Acknowledgments

The authors would like to thank the editors and the anonymous reviewers for their outstanding comments and suggestions that helped to improve the technical quality and presentation of this manuscript. We also thank the United States Geological Survey (www.usgs.gov), the National Aeronautics and Space Administration (www.nasa.gov), and the Chinese Academy of Science (www.ids.ceode.ac.cn) for the free availability of Landsat remote sensing images. We thank LetPub (www.letpub.com) for linguistic assistance and pre-submission expert review.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available on request from the corresponding author.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant nos 41971296, 42122009, 42171311, and 42271340], in part by the Zhejiang Province Pioneering Soldier and Leading Goose R&D Project [grant no 2023C01027], in part by the Public Projects of Ningbo City [grant no 2021S089], and in part by the Ningbo Science and Technology Innovation 2025 Major Special Project [grant nos 2021Z107 and 2022Z032].