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多模式建筑损害评估方法融合了自适应注意力机制和状态空间建模
Rongping Zhu1,2, Xiaoji Lan1,2
1School of Civil and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了适应差异状态与空间融合网络 (ADSFNet),用于改进建筑损坏评估 (BDA). 通过融合光学和SAR数据,ADSFNet提高了准确性,在灾后分析中表现优于现有的方法.
科学领域:
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
- 灾害管理 灾害管理
背景情况:
- 准确的建筑损坏评估 (BDA) 对于有效的灾后反应至关重要.
- 目前的BDA方法与背景噪声,有限的空间依赖模型和恶劣天气作斗争.
- 需要强大的BDA解决方案来整合多模式数据是非常重要的.
研究的目的:
- 为增强BDA提出适应差异国家与空间融合网络 (ADSFNet).
- 通过处理光学和合成孔径雷达 (SAR) 数据来提高对天气诱导限制的弹性.
- 开发一个智能紧急决策系统,使用以灾难为中心的知识图和大型语言模型.
主要方法:
- ADSFNet使用自适应差异注意力融合 (ADAF) 模块,专注于建筑损坏,同时减少背景干扰.
- 混合选择性状态空间卷积 (HSSC) 模块将卷积神经网络 (CNN) 结合起来,用于本地纹理和Mamba用于全球空间依赖.
- 网络处理融合光学和SAR图像以进行全面的损坏分析.
主要成果:
- 在BRIGHT和xBD数据集上,ADSFNet实现了卓越的性能,F1得分分别为71.36%和73.98%.
- 提出的方法比最先进的技术有1.29%和0.6%的改进.
- 该模型成功与大型语言模型集成,用于智能灾害管理系统.
结论:
- 通过有效地融合多模式数据,ADSFNet在建筑损害评估方面取得了重大进展.
- 该网络能够模拟本地细节和全球背景,在具有挑战性的条件下提高准确性.
- 开发的智能管理系统为改善应急响应和决策提供了一条新的途径.
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