Related papers: SpecTM: Spectral Targeted Masking for Trustworthy Foundation Models

SpecTM: Spectral Targeted Masking for Trustworthy Foundation Models

URL: http://arxiv.org/abs/2603.22097v1
Date: Mon, 23 Mar 2026 15:26:51 GMT
Title: SpecTM: Spectral Targeted Masking for Trustworthy Foundation Models
Abstract summary: SpecTM is a physics-informed masking design that encourages the reconstruction of targeted bands from cross-spectral context.<n>It achieves R2 = 0.695 (current week) and R2 = 0.620 (8-day-ahead) predictions surpassing all baseline models by (+34% (0.51 Ridge) and +99% (SVR 0.31)<n>It outperforms strong baselines with 2.2x superior label efficiency under extreme scarcity.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Foundation models are now increasingly being developed for Earth observation (EO), yet they often rely on stochastic masking that do not explicitly enforce physics constraints; a critical trustworthiness limitation, in particular for predictive models that guide public health decisions. In this work, we propose SpecTM (Spectral Targeted Masking), a physics-informed masking design that encourages the reconstruction of targeted bands from cross-spectral context during pretraining. To achieve this, we developed an adaptable multi-task (band reconstruction, bio-optical index inference, and 8-day-ahead temporal prediction) self-supervised learning (SSL) framework that encodes spectrally intrinsic representations via joint optimization, and evaluated it on a downstream microcystin concentration regression model using NASA PACE hyperspectral imagery over Lake Erie. SpecTM achieves R^2 = 0.695 (current week) and R^2 = 0.620 (8-day-ahead) predictions surpassing all baseline models by (+34% (0.51 Ridge) and +99% (SVR 0.31)) respectively. Our ablation experiments show targeted masking improves predictions by +0.037 R^2 over random masking. Furthermore, it outperforms strong baselines with 2.2x superior label efficiency under extreme scarcity. SpecTM enables physics-informed representation learning across EO domains and improves the interpretability of foundation models.

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