Accurate and timely assessment of post-disaster building damage is of great significance for national development and social security concerns. However, due to the high timeliness requirements of disaster emergency response and the conflict that sufficient computing resources are not easily available in harsh environments, and therefore the lightweight AI-driven post-disaster building damage assessment model is highly needed. In this paper, we introduced a knowledge distillation-based lightweight approach for assessing building damage from xBD high-resolution satellite images with the purpose of reducing the dependence on computing resources in disaster emergency response scenarios. Specifically, an ensemble Teacher-Student knowledge distillation method was designed and compared with the xBD baseline model. The result has shown that, the knowledge distillation reduces the parameter number of the original model by 30%, and the inference speed is increased by 30%-40%. In the building localization task, the accuracy of teacher and student model are 0.879 and 0.832 (IOU) respectively. In the damage classification task, the accuracy of teacher and student are 0.798 and 0.775 respectively. In addition, we proposed a dual-teacher-student knowledge distillation strategy, which cannot use the pre-training skills of curriculum learning in student model training, but achieve the same effect through more direct knowledge transfer. In the experiment, our dual-teacher-student method improves the knowledge distillation baseline by 3.7% with 30 epoch training. With only 70% parameters, our student model performs close to the teacher model at a degradation within 5%.This study verifies the effectiveness and prospect of knowledge distillation method in building damage assessment for disaster emergency.

准确、及时地评估灾后建筑损毁情况，对国家发展和社会保障关切具有重要意义。然而，由于灾害应急响应的时效性要求高，以及在恶劣环境下难以获得足够的计算资源的矛盾，因此非常需要轻量级的人工智能驱动的灾后建筑损伤评估模型。在本文中，我们介绍了一种基于知识蒸馏的轻量级方法，用于从 xBD 高分辨率卫星图像评估建筑物损坏，目的是减少灾害应急响应场景中对计算资源的依赖。具体来说，设计了一种集成师生知识蒸馏方法，并与 xBD 基线模型进行了比较。结果表明，知识蒸馏使原始模型的参数数量减少了30%，推理速度提高了30%-40%。在建筑物定位任务中，教师和学生模型的准确率分别为 0.879 和 0.832（IOU）。在损伤分类任务中，教师和学生的准确率分别为 0.798 和 0.775。此外，我们提出了一种师生双元知识蒸馏策略，在学生模型训练中不能使用课程学习的预训练技能，而是通过更直接的知识转移来达到同样的效果。在实验中，我们的双师生方法通过 30 个 epoch 的训练将知识蒸馏基线提高了 3.7%。只有 70% 的参数，我们的学生模型的性能接近于教师模型，下降幅度在 5% 以内。