Lossy neural compression for geospatial analytics: a review

Journal article

Over the past decades, there has been an explosion in the amount of available Earth observation (EO) data. The unprecedented coverage of Earth’s surface and atmosphere by satellite imagery has resulted in large volumes of data that must be transmitted to ground stations, stored in data centers, and distributed to end users. Modern Earth system models (ESMs) face similar challenges, operating at high spatial and temporal resolutions, producing petabytes of data per simulated day. Data compression has gained relevance over the past decade, with neural compression (NC) emerging from deep learning and information theory, making EO data and ESM outputs ideal candidates because of their abundance of unlabeled data.

In this review, we outline recent developments in NC applied to geospatial data. We introduce the fundamental concepts of NC, including seminal works in its traditional applications to image and video compression domains with a focus on lossy compression. We discuss the unique characteristics of EO and ESM data, contrasting them with “natural images,” and we explain the additional challenges and opportunities they present. Additionally, we review current applications of NC across various EO modalities and explore the limited efforts in ESM compression to date. The advent of self-supervised learning (SSL) and foundation models (FMs) has advanced methods to efficiently distill representations from vast amounts of unlabeled data. We connect these developments to NC for EO, highlighting the similarities between the two fields and elaborate on the potential of transferring compressed feature representations for machine-to-machine communication. Based on insights drawn from this review, we devise future directions relevant to applications in EO and ESMs.

Authors: Gomes, C; Wittmann, I; Robert, D; Jakubik, J; Reichelt, T

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Journal: IEEE Geoscience and Remote Sensing Magazine
• Volume: 13
• Issue: 3
• Pages: 97-135
• Publication date: 2025-03-20
• DOI: 10.1109/mgrs.2025.3546527
• EISSN: 2168-6831
• ISSN: 2473-2397
• Language: English
• Keywords: reviews; deep learning; geospatial analysis; atmospheric modeling; frequency modulation; data models; codecs; satellites; artificial neural networks; data compression