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

• URL: http://arxiv.org/abs/2412.13394v1
• Date: Wed, 18 Dec 2024 00:10:44 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 the challenge of identifying inputs that differ from in-distribution (ID) data.<n>We propose TARDIS, a post-hoc OOD detection method for scalable geospatial deployments.<n>Our method takes a pre-trained model, ID data WILD samples, and WILD samples, disentangling the latter into surrogate ID and surrogate OOD labels.<n>To demonstrate scalability, we deploy TARDIS on the Fields of the World dataset, offering actionable insights into pre-trained model behavior for large-scale deployments.
• Score: 4.457854503856095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training robust deep learning models is critical 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 challenge by identifying inputs that differ from in-distribution (ID) data. However, existing methods either assume access to OOD data or compromise primary task performance, making them unsuitable for real-world deployment. We propose TARDIS, a post-hoc OOD detection method for scalable geospatial deployments. The core novelty lies in generating surrogate labels by integrating information from ID data and unknown distributions, enabling OOD detection at scale. Our method takes a pre-trained model, ID data, and WILD samples, disentangling the latter into surrogate ID and surrogate OOD labels based on internal activations, and fits a binary classifier as an OOD detector. We validate TARDIS on EuroSAT and xBD datasets, across 17 experimental setups covering covariate and semantic shifts, showing that it performs close to the theoretical upper bound in assigning surrogate ID and OOD samples in 13 cases. To demonstrate scalability, we deploy TARDIS on the Fields of the World dataset, offering actionable insights into pre-trained model behavior for large-scale deployments. The code is publicly available at https://github.com/microsoft/geospatial-ood-detection.

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