  Satellite remote sensing is playing an increasing role in the rapid mapping
of damage after natural disasters. In particular, synthetic aperture radar
(SAR) can image the Earth's surface and map damage in all weather conditions,
day and night. However, current SAR damage mapping methods struggle to separate
damage from other changes in the Earth's surface. In this study, we propose a
novel approach to damage mapping, combining deep learning with the full time
history of SAR observations of an impacted region in order to detect anomalous
variations in the Earth's surface properties due to a natural disaster. We
quantify Earth surface change using time series of Interferometric SAR
coherence, then use a recurrent neural network (RNN) as a probabilistic anomaly
detector on these coherence time series. The RNN is first trained on pre-event
coherence time series, and then forecasts a probability distribution of the
coherence between pre- and post-event SAR images. The difference between the
forecast and observed co-event coherence provides a measure of the confidence
in the identification of damage. The method allows the user to choose a damage
detection threshold that is customized for each location, based on the local
behavior of coherence through time before the event. We apply this method to
calculate estimates of damage for three earthquakes using multi-year time
series of Sentinel-1 SAR acquisitions. Our approach shows good agreement with
observed damage and quantitative improvement compared to using pre- to co-event
coherence loss as a damage proxy.
