FUSION OF MODELING AND REMOTE SENSING FOR DAMAGE ESTIMATION

Moya L.; Mas E.; Koshimura S.; Yamazaki F.; Liu W.; Geiß C. · World Conference on Earthquake Engineering proceedings (2021)

Resumen

Disaster risk analysis involves a set of disaster events, their consequences, and their probabilities of occurrence over a defined period. The main, or traditional role of disaster risk is to serve as a guide for decisions about safety. Whether the infrastructure requires retrofitting or whether having insurance is cost-effective are such examples. An important stage in estimating the disaster risk is the estimation of damage as a function of the demand (i.e., ground motion for earthquake or inundation depth for tsunamis). These damage functions have been playing the main role in one of the recent trends. With the technological progress in instrumentation, numerical modeling and communication networks, in the aftermath of a large-scale disaster, it is possible to estimate a demand map in real/near-real time. Then, with the aid of the damage functions, an estimation of the damage map can be computed. However, with damage functions, we can only compute the probability that an asset is damaged. The best that can be done with such information is to report the expected amount of damaged assets within a given area. It is not possible to indicate which asset was damaged. A more realistic estimation of damage due to an arbitrary disaster can be obtained from remote sensing data, such as satellite images. With remote sensing data recorded after a disaster (images recorded before a disaster are usually available as well), the real effect of the disaster can be detected. However, the procedure is not straightforward. Usually, a set of features are computed from remote sensing data for each asset. Such features are used as input of a discriminant function that predicts whether the asset is damaged or not. The discriminant function is best calibrated using machine learning methods, but they require training data. Training data is very difficult to collect right after a disaster because all the efforts are focused on rescue and relief activities. In this paper we summarize the studies we have performed regarding the fusion of the two referred disciplines to implement a fully automatic damage mapping procedure. © The 17th World Conference on Earthquake Engineering.

Instituciones: Universidad Nacional de Ingeniería