| 研究課題/領域番号 |
22K14327
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分22040:水工学関連
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| 研究機関 | 東京海洋大学 |
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研究代表者 |
呉 連慧 東京海洋大学, 学術研究院, 助教 (50907615)
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| 研究期間 (年度) |
2022-04-01 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,550千円 (直接経費: 3,500千円、間接経費: 1,050千円)
2024年度: 650千円 (直接経費: 500千円、間接経費: 150千円)
2023年度: 2,080千円 (直接経費: 1,600千円、間接経費: 480千円)
2022年度: 1,820千円 (直接経費: 1,400千円、間接経費: 420千円)
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| キーワード | shoreline extraction / satellite SAR / sandy beach / coastal structure / X-band SAR / UAV / neural network / coast protection / Shoreline / Neural network |
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| 研究開始時の研究の概要 |
This research aims to develop a high-resolution shoreline extraction technique by using convolutional neural network (CNN) based on X-band SAR images. The developed technique is expected to improve our monitoring ability of the shoreline change for sustainable management of the coastal zone.
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| 研究実績の概要 |
(1)The shoreline extraction model from Sentinel-1 SAR imagery based on DeepLab-v3+ was applied to 15 beaches in Japan to investigate its applicability and versatility. By using different combination of SAR images from beaches with varying characteristics, several models were constructed. The developed model demonstrates high accuracy in shoreline extraction, particularly when trained with a diverse set of images from various beaches.(2)A shoreline extraction model using 1-meter resolution X-band SAR images was proposed. Both for coasts with and without structures, the extraction accuray can be as high as 3-4 pixels. These results demonstrate the potential of using high-resolution X-band SAR images for coastal observations.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
Shoreline extraction mdoel based on DeepLab-v3+ shows high accuary both for C-band and X-band SAR images. The applicability and versatility were throughoutly examined. Based on the above, it can be concluded that overall, progress is generally proceeding as planned.
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| 今後の研究の推進方策 |
More X-band SAR images are expected to be collected to improve the model accuracy. In addition,the necessary amount of SAR data for construction of a shoreline extraction model for a specific sit will be investigated.
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