Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models

arXiv:2404.12667v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cs.ai cs.cv cs.lg data diffusion diffusion models disasters distribution earth earth observation events features images landscape major observation samples sensing type visual will