Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation

Training robust deep learning models is crucial in Earth Observation, where globally deployed models often face distribution shifts that degrade performance, especially in low-data regions. Out-of-distribution (OOD) detection addresses this by identifying inputs that deviate from in-distribution (ID) data. However, existing methods either assume access to OOD data or compromise primary task performance, limiting real-world use. We introduce TARDIS, a post-hoc OOD detection method designed for scalable geospatial deployment. Our core innovation lies in generating surrogate distribution labels by leveraging ID data within the feature space. TARDIS takes a pre-trained model, ID data, and data from an unknown distribution (WILD), separates WILD into surrogate ID and OOD labels based on internal activations, and trains a binary classifier to detect distribution shifts. We validate on EuroSAT and xBD across 17 setups covering covariate and semantic shifts, showing near-upper-bound surrogate labeling performance in 13 cases and matching the performance of top post-hoc activation- and scoring-based methods. Finally, deploying TARDIS on Fields of the World reveals actionable insights into pre-trained model behavior at scale. The code is available at \href{this https URL}{this https URL}

@article{ekim2025_2412.13394,
  title={ Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation },
  author={ Burak Ekim and Girmaw Abebe Tadesse and Caleb Robinson and Gilles Hacheme and Michael Schmitt and Rahul Dodhia and Juan M. Lavista Ferres },
  journal={arXiv preprint arXiv:2412.13394},
  year={ 2025 }
}