  Earth observation technologies, such as optical imaging and synthetic
aperture radar (SAR), provide excellent means to monitor ever-growing urban
environments continuously. Notably, in the case of large-scale disasters (e.g.,
tsunamis and earthquakes), in which a response is highly time-critical, images
from both data modalities can complement each other to accurately convey the
full damage condition in the disaster's aftermath. However, due to several
factors, such as weather and satellite coverage, it is often uncertain which
data modality will be the first available for rapid disaster response efforts.
Hence, novel methodologies that can utilize all accessible EO datasets are
essential for disaster management. In this study, we have developed a global
multisensor and multitemporal dataset for building damage mapping. We included
building damage characteristics from three disaster types, namely, earthquakes,
tsunamis, and typhoons, and considered three building damage categories. The
global dataset contains high-resolution optical imagery and
high-to-moderate-resolution multiband SAR data acquired before and after each
disaster. Using this comprehensive dataset, we analyzed five data modality
scenarios for damage mapping: single-mode (optical and SAR datasets),
cross-modal (pre-disaster optical and post-disaster SAR datasets), and mode
fusion scenarios. We defined a damage mapping framework for the semantic
segmentation of damaged buildings based on a deep convolutional neural network
algorithm. We compare our approach to another state-of-the-art baseline model
for damage mapping. The results indicated that our dataset, together with a
deep learning network, enabled acceptable predictions for all the data modality
scenarios.
