Collapsed buildings should be detected with the highest priority during earthquake emergency response, due to the associated fatality rates. Although deep learning-based damage detection using vertical aerial images can achieve high performance, as depth information cannot be obtained, it is difficult to detect collapsed buildings when their roofs are not heavily damaged. Airborne LiDAR can efficiently obtain the 3D geometries of buildings (in the form of point clouds) and thus has greater potential to detect various collapsed buildings. However, there have been few previous studies on deep learning-based damage detection using point cloud data, due to a lack of large-scale datasets. Therefore, in this paper, we aim to develop a dataset tailored to point cloud-based building damage detection, in order to investigate the potential of point cloud data in collapsed building detection. Two types of building data are created: building roof and building patch, which contains the building and its surroundings. Comprehensive experiments are conducted under various data availability scenarios (pre–post-building patch, post-building roof, and post-building patch) with varying reference data. The pre–post scenario tries to detect damage using pre-event and post-event data, whereas post-building patch and roof only use post-event data. Damage detection is implemented using both basic and modern 3D point cloud-based deep learning algorithms. To adapt a single-input network, which can only accept one building’s data for a prediction, to the pre–post (double-input) scenario, a general extension framework is proposed. Moreover, a simple visual explanation method is proposed, in order to conduct sensitivity analyses for validating the reliability of model decisions under the post-only scenario. Finally, the generalization ability of the proposed approach is tested using buildings with different architectural styles acquired by a distinct sensor. The results show that point cloud-based methods can achieve high accuracy and are robust under training data reduction. The sensitivity analysis reveals that the trained models are able to locate roof deformations precisely, but have difficulty recognizing global damage, such as that relating to the roof inclination. Additionally, it is revealed that the model decisions are overly dependent on debris-like objects when surroundings information is available, which leads to misclassifications. By training on the developed dataset, the model can achieve moderate accuracy on another dataset with different architectural styles without additional training.

A damage function that indicates the relationship between flood depth and damage is important to quantify natural catastrophe risks. Previous studies estimated the flood damage ratio using theoretical methodologies and in-person interviews. Although it is difficult to obtain actual damage information, it can be reflected in damage functions using insurance loss data. This work aims to develop damage functions for buildings and their contents using insurance loss data from the 2015 Kanto–Tohoku heavy rainfall incident. First, we collected information on the insurance losses and calculated damage ratio by property. Second, we analyzed inundation from the Kinugawa river using the flood model. Finally, we combined the simulation results with the damage ratio and developed flood damage functions as useful risk-assessment tools.

洪水被害と浸水深との関係性を示す被害関数は，自然災害リスクを計算する上で重要であるが，その研究事例は少ない．既往研究では，家屋損害は主に氾濫流の数値解析あるいはヒアリング調査により算出されてきている．損害保険会社は膨大な保険金支払情報を所有しており，それらを活用することにより，実被害情報を被害関数に反映することが期待できる．そこで本研究では，洪水被害関数の蓄積を目的に，平成27年9月関東・東北豪雨における保険金支払情報を活用し，建物および内容物の洪水被害関数を構築した．まず，保険金支払情報を入手し，物件毎に損害率を計算した．次に，洪水の再現計算を行い，損害率と推定浸水深のデータセットを作成した．最後に，建物・内容物別に被害関数を構築した．