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A Practical Guide to Hyperspectral Foundation Models

(2025)

Authors:

Conrad Albrecht, Ruben Gonzalez, Nassim Ait Ali Braham, Ranjini Bangalore, Thomas Brunschwiler

Abstract:

Hyperspectral imagery (HSI) provides rich spectral information that is the basis for applications such as mineral mapping, trace gas identification, and precision agriculture. Yet, the development of HSI Foundation Models (FMs) is less advanced compared to multi-spectral remote sensing modalities.In this study, we leverage the SpectralEarth dataset [1] to explore practical aspects of training robust HSI FMs. In particular, we shed light on the role of:the impact of model architecture (transformers vs. convolutional networks), self-supervised learning methods (contrastive vs. masked autoencoders), model size & training data volume, and the resulting computational requirements. Through extensive experiments, this study aims to provide concrete guidelines for the development and effective application of FMs in the HSI domain. Moreover, we report on findings to identify downstream applications where hyperspectral imagery has an edge over multi-spectral photos [2], and where such an advantage is less likely to expect. References[1] Braham, Nassim Ait Ali, et al. "SpectralEarth: Training Hyperspectral Foundation Models at Scale." arXiv preprint arXiv:2408.08447 (2024)[2] Bangalore, Ranjini, et al. "Hyperspectral foundation model trained by spectral reconstruction for greenhouse gas emission estimation", annual meeting of the American Geophysical Union (2024)
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Anthropogenic aerosol effects on convective clouds and precipitation in global km-scale simulations with ICON-HAM-lite

Copernicus Publications (2025)

Authors:

Philip Stier, Philipp Weiss, Sadhitro De, Maor Sela, William Jones
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ClimateBenchPress: A Benchmark for Compression of Climate Data

Copernicus Publications (2025)

Authors:

Tim Reichelt, Juniper Tyree, Milan Kloewer, Peter Dueben, Bryan Lawrence, Dorit Hammerling, Alisson Baker, Sara Faghih-Naini, Philip Stier
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Convective mass flux and cloud anvil development in km-scale climate models

Copernicus Publications (2025)

Authors:

Mathilde Ritman, William Jones, Philip Stier
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Earth Observation embeddings at the test: A novel benchmark to evaluate (neural) compression for satellite imagery

(2025)

Authors:

Rikard Vinge, Michael L Marszalek, Jannik Schneider, Conrad M Albrecht

Abstract:

With the rapidly growing production and utilization of Earth Observation (EO) data, the past decade sparked interest in the efficient compression of EO data into low-dimensional embeddings. In a parallel development, EO Foundation Models (FM), trained on large amounts of unlabeled data to be used in a wide range of applications, also utilize low-dimensional embeddings to distill representations of EO data [1, 2, 3]. In one aspect, EO FMs may serve as (lossy) neural compressors to improve data transfer and lower storage needs – effectively reducing the carbon footprint of EO data [4]. While the development in EO FMs rapidly advances, there is need for a novel benchmark scheme to evaluate the quality of (compressed) embeddings. The statement “foundational” or “general purpose representation” needs a test. As part of the Horizon Europe project “Embed2Scale” [5], co-funded by the European Union (Horizon Europe contract No. 101131841), the Swiss State Secretariat for Education (SERI), and UK Research and Innovation (UKRI), we present a novel approach to benchmark learnt compression of multimodal Copernicus Sentinel data for various relevant application domains. In the form of a competition, contestants provide embeddings that are evaluated on a diverse set of problems based on real-life use cases relevant for the research community, governments, and corporate businesses. The problems are hidden from the contestants to evaluate the applicability of the embeddings to unknown problems. The benchmark statistically evaluates the performance of downstream tasks through fine-tuning of neural networks that fit into commodity hardware. We underline a practically relevant scenario where end users rarely have access to costly and energy-intensive acceleration hardware. The overall performance, i.e. the evaluation across all the benchmark’s problems, is crucial and ensures a diverse and fair evaluation of the embeddings. After the competition, the datasets in the benchmark are published and made available to the community. [1] X. Sun et al., “RingMo: A remote sensing foundation model with masked image modeling,” IEEE Transactions on Geoscience and Remote Sensing, 2022. [2] D. Wang et al., “Advancing plain vision transformer toward remote sensing foundation model,” IEEE Transactions on Geoscience and Remote Sensing, 2022. [3] C. Bodnar et al., “Aurora: A foundation model of the atmosphere,” Tech. Rep., 2024. [4] R. Wilkinson, M.M. Mleczko, R.J.W. Brewin, K.J. Gaston, M. Mueller, J.D. Shutler, X. Yan, K. Anderson, Environmental impacts of earth observation data in the constellation and cloud computing era,Science of The Total Environment, Volume 909,2024,168584,ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2023.168584 [5] https://embed2scale.eu/
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