Automatic damage assessment based on UAV-derived 3D point clouds can provide fast information on the damage situation after an earthquake. However, the assessment of multiple damage grades is challenging due to the variety in damage patterns and limited transferability of existing methods to other geographic regions or data sources. We present a novel approach to automatically assess multi-class building damage from real-world multi-temporal point clouds using a machine learning model trained on virtual laser scanning (VLS) data. We (1) identify object-specific change features, (2) separate changed and unchanged building parts, (3) train a random forest machine learning model with VLS data based on object-specific change features, and (4) use the classifier to assess building damage in real-world point clouds from photogrammetry-based dense image matching (DIM). We evaluate classifiers trained on different input data with respect to their capacity to classify three damage grades (heavy, extreme, destruction) in pre- and post-event DIM point clouds of a real earthquake event. Our approach is transferable with respect to multi-source input point clouds used for training (VLS) and application (DIM) of the model. We further achieve geographic transferability of the model by training it on simulated data of geometric change which characterises relevant damage grades across different geographic regions. The model yields high multi-target classification accuracies (overall accuracy: 92.0% - 95.1%). Its performance improves only slightly when using real-world region-specific training data (< 3% higher overall accuracies) and when using real-world region-specific training data (< 2% higher overall accuracies). We consider our approach relevant for applications where timely information on the damage situation is required and sufficient real-world training data is not available.

翻译：基于无人机衍生三维点云的自动损伤评估可在地震后快速提供灾情信息。然而，由于损伤模式的多样性和现有方法对其他地理区域或数据源的有限迁移性，多级损伤评估仍具挑战性。我们提出了一种新方法，利用基于虚拟激光扫描（VLS）数据训练的机器学习模型，从真实世界多时相点云中自动评估多类建筑损伤。具体步骤包括：(1) 识别对象特定变化特征；(2) 分离变化与未变化的建筑部分；(3) 基于对象特定变化特征，使用VLS数据训练随机森林机器学习模型；(4) 利用该分类器评估基于摄影测量密集图像匹配（DIM）的真实世界点云中的建筑损伤。我们评估了基于不同输入数据训练的分类器对某真实地震事件灾前与灾后DIM点云中三级损伤（重度、极重度、毁坏）的分类能力。本方法在模型训练（VLS）与应用（DIM）所采用的多源输入点云间具有迁移性。进一步地，通过利用表征不同地理区域相关损伤等级的几何变化模拟数据进行训练，我们实现了模型的地理迁移性。该模型的多目标分类准确率较高（总体准确率：92.0%-95.1%）。当使用真实世界区域特定训练数据时，模型性能仅略有提升（总体准确率提升<3%），当使用区域特定真实数据时提升更小（总体准确率提升<2%）。我们认为该方法适用于需要快速获取灾情信息且缺乏足够真实世界训练数据的应用场景。