最先进的语义细分网络的比较评估,用于长期的山体滑坡地图制作
Zekun Hu1,2, Bangjin Yi3, Hui Li4
1Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
本研究评估了用于创建长期滑坡地图 (LAM) 的深度学习模型. 在U-Net 3+中,U-Net 3+在滑坡检测中对时间转移学习具有前景,为高效地图制作提供了洞察力.
科学领域:
- 地质科学 地质科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 长期滑坡地图 (LAM) 对于评估滑坡活动,植被影响和区域稳定至关重要.
- 在许多领域,有限的LAM可用性阻碍了有效的风险评估.
- 现有的机器学习和深度学习 (DL) 方法来检测山体滑坡是有前途的,但需要进一步的时间可转移性评估.
研究的目的:
- 为了评估语义细分DL模型的时间转移性,用于长期的滑坡地图 (LAM) 制作.
- 确定最有效的DL模型和创建可靠的LAM的最佳设置.
- 解决关于DL模型在LAM生成中的时间可转移性的研究缺口.
主要方法:
- 在U-Net,U-Net 3+和TransU-Net模型上进行了广泛的测试.
- 利用了温chuan地震中心附近的10年滑坡库存数据集.
- 评估了不同数据量,补丁大小和时间间隔对模型性能的影响.
主要成果:
- 演示了LAM生产转移学习的可行性和局限性,U-Net 3+特别有效.
- 确定了在LAM生成中平衡效率和性能的最佳参数设置.
- 提供了关于不同DL架构的时间可转移性的经验证据.
结论:
- 转移学习,特别是U-Net 3+,是大规模LAM生产的可行方法.
- 对于高效可靠的LAM生成,建议使用特定的数据量,补丁大小和时间间隔.
- 调查结果为在脆弱地区制定强有力的山体滑坡测绘策略提供了宝贵的参考资料.
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