  Earth Observation imagery can capture rare and unusual events, such as
disasters and major landscape changes, whose visual appearance contrasts with
the usual observations. Deep models trained on common remote sensing data will
output drastically different features for these out-of-distribution samples,
compared to those closer to their training dataset. Detecting them could
therefore help anticipate changes in the observations, either geographical or
environmental. In this work, we show that the reconstruction error of diffusion
models can effectively serve as unsupervised out-of-distribution detectors for
remote sensing images, using them as a plausibility score. Moreover, we
introduce ODEED, a novel reconstruction-based scorer using the probability-flow
ODE of diffusion models. We validate it experimentally on SpaceNet 8 with
various scenarios, such as classical OOD detection with geographical shift and
near-OOD setups: pre/post-flood and non-flooded/flooded image recognition. We
show that our ODEED scorer significantly outperforms other diffusion-based and
discriminative baselines on the more challenging near-OOD scenarios of flood
image detection, where OOD images are close to the distribution tail. We aim to
pave the way towards better use of generative models for anomaly detection in
remote sensing.
