DDNet:基于双时间联合关注网络的建筑物灾害损害检测
Xiaosan Ge1, Lin Zhou2,3, Di Meng2
1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China. gxs@hpu.edu.cn.
Scientific reports
|November 27, 2025
概括
本研究介绍了DDNet,这是一个新的两阶段网络,用于使用卫星图像进行准确的建筑损坏评估. DDNet 改善了建筑物本地化和损坏分类,以便在灾后做出更好的反应.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 灾害管理 灾害管理
背景情况:
- 灾后准确的建筑损坏评估对于应急响应至关重要.
- 卫星图像分析,包括建筑物定位和损坏分类,是一种常见的方法.
- 现有的方法面临着阶级不平衡,边界偏差和从时间图像中提取特征的挑战.
研究的目的:
- 提出一个新的两阶段网络,DDNet,用于增强建筑损坏检测.
- 为了解决建筑本地化和损坏分类准确性的局限性.
- 提高灾后建筑损害评估的整体有效性.
主要方法:
- 开发了一个两阶段网络 (DDNet).
- 第一个阶段使用差异上抽样和统一的焦点损失来改善建筑物定位.
- 第二阶段包括一个共同的注意模块,用于从多时空卫星图像中有效地提取特征.
主要成果:
- 在xBD数据集上,DDNet获得了79.56%的F1总得分.
- 定位F1得分达到86.38%,而损伤分类F1得分为76.64%.
- 该网络有效地缓解了阶级不平衡,并改善了特征挖掘.
结论:
- 拟议的DDNet框架显著提高了建筑损坏评估的准确性.
- 这种两阶段的方法有效地解决了本地化和分类方面的挑战.
- 使用卫星图像,DDNet在灾后建筑损坏检测方面表现强.
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