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

• URL: http://arxiv.org/abs/2412.13394v2
• Date: Tue, 08 Apr 2025 21:00:47 GMT
• Title: Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation
• Authors: Burak Ekim, Girmaw Abebe Tadesse, Caleb Robinson, Gilles Hacheme, Michael Schmitt, Rahul Dodhia, Juan M. Lavista Ferres,
• Abstract summary: Out-of-distribution (OOD) detection addresses this by identifying inputs that deviate from in-distribution (ID) data.<n>We introduce TARDIS, a post-hoc OOD detection method designed for scalable geospatial deployment.<n>Our core innovation lies in generating surrogate distribution labels by leveraging ID data within the feature space.
• Score: 4.457854503856095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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{https://github.com/microsoft/geospatial-ood-detection}{https://github.com/microsoft/geospatial-ood-detection}

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