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3D点云震害建筑物深度学习样本增强方法

  • role： First author第一作者
  • Affiliation：

    Laboratory of Earthquake Numerical Prediction and Risk Prediction, Institute of Earthquake Forecasting, CEA, Beijing 100036, China

  • Email：15738510390@163.com
  • Introduction：崔驿宁，研究方向为遥感与GIS应用。E-mail：15738510390@163.com
CUI Yining1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Laboratory of Earthquake Numerical Prediction and Risk Prediction, Institute of Earthquake Forecasting, CEA, Beijing 100036, China

  • Email：axdothy@163.com
  • Introduction：窦爱霞，研究方向为多源遥感地震灾害监测与风险评估、空间技术综合减灾应用。E-mail：axdothy@163.com
DOU Aixia1*,  
  • Affiliation：

    Beijing MAG Tianhong Science&Technology Development Co., Ltd, Beijing 100089, China

YANG Shenning2

resumen

Para abordar el problema de la identificación automática de tipos de daños en edificios a partir de nubes de puntos LiDAR en escenarios complejos después de un terremoto, para satisfacer las demandas de rapidez y precisión en situaciones de emergencia, para decir adiós a la extracción manual tradicional de características de daños y aprovechar al máximo la información de daños en edificios en áreas afectadas, y para lograr luego la identificación automática e inteligente de edificios. Este artículo aplica el método de aprendizaje profundo de nubes de puntos 3D a la identificación de daños en edificios, ha creado una base de datos de nubes de puntos que incluye tipos de daños como colapso, colapso parcial y no colapso. La red PointNet++ investiga el impacto del volumen y el equilibrio de las muestras en la precisión de la identificación, y propone un método de mejora de muestras de edificios dañados, enriqueciendo las formas de puntos en muestras de diferentes clases. Basándose en datos LiDAR después del terremoto de magnitud 7,0 en Haití en 2010, se comparó la precisión de la clasificación antes y después de la mejora de muestras en la red PointNet++, los resultados mostraron una mejora del 27% y 17% respectivamente en la precisión de la clasificación de colapsos y colapsos parciales después de la mejora de muestras, y un aumento del 15% en la precisión promedio de la clasificación y el coeficiente Kappa. Los resultados de los experimentos muestran que el modelo de aprendizaje profundo tridimensional de daños en edificios muestra una buena mejora en la clasificación cuando el volumen y el equilibrio de las muestras son suficientes para todas las clases de muestras.

palabra clave

remote sensing;classification and recognition;PointNet++;sample enhancement;LiDAR point cloud;seismic damage buildings

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