Lossy Neural Compression for Geospatial Analytics: A Review
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912635271774208 |
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| author | Gomes, Carlos Wittmann, Isabelle Robert, Damien Jakubik, Johannes Reichelt, Tim Martone, Michele Maurogiovanni, Stefano Vinge, Rikard Hurst, Jonas Scheurer, Erik Sedona, Rocco Brunschwiler, Thomas Kesselheim, Stefan Batic, Matej Stier, Philip Wegner, Jan Dirk Cavallaro, Gabriele Pebesma, Edzer Marszalek, Michael Belenguer-Plomer, Miguel A Adriko, Kennedy Fraccaro, Paolo Kienzler, Romeo Briq, Rania Benassou, Sabrina Lazzarini, Michele Albrecht, Conrad M |
| author_facet | Gomes, Carlos Wittmann, Isabelle Robert, Damien Jakubik, Johannes Reichelt, Tim Martone, Michele Maurogiovanni, Stefano Vinge, Rikard Hurst, Jonas Scheurer, Erik Sedona, Rocco Brunschwiler, Thomas Kesselheim, Stefan Batic, Matej Stier, Philip Wegner, Jan Dirk Cavallaro, Gabriele Pebesma, Edzer Marszalek, Michael Belenguer-Plomer, Miguel A Adriko, Kennedy Fraccaro, Paolo Kienzler, Romeo Briq, Rania Benassou, Sabrina Lazzarini, Michele Albrecht, Conrad M |
| contents | Over the past decades, there has been an explosion in the amount of available Earth Observation (EO) data. The unprecedented coverage of the 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 due to 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 focus on lossy compression. We discuss the unique characteristics of EO and ESM data, contrasting them with "natural images", and explain the additional challenges and opportunities they present. Moreover, 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 (FM) has advanced methods to efficiently distill representations from vast 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 ESM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01505 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lossy Neural Compression for Geospatial Analytics: A Review Gomes, Carlos Wittmann, Isabelle Robert, Damien Jakubik, Johannes Reichelt, Tim Martone, Michele Maurogiovanni, Stefano Vinge, Rikard Hurst, Jonas Scheurer, Erik Sedona, Rocco Brunschwiler, Thomas Kesselheim, Stefan Batic, Matej Stier, Philip Wegner, Jan Dirk Cavallaro, Gabriele Pebesma, Edzer Marszalek, Michael Belenguer-Plomer, Miguel A Adriko, Kennedy Fraccaro, Paolo Kienzler, Romeo Briq, Rania Benassou, Sabrina Lazzarini, Michele Albrecht, Conrad M Signal Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Geophysics Over the past decades, there has been an explosion in the amount of available Earth Observation (EO) data. The unprecedented coverage of the 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 due to 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 focus on lossy compression. We discuss the unique characteristics of EO and ESM data, contrasting them with "natural images", and explain the additional challenges and opportunities they present. Moreover, 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 (FM) has advanced methods to efficiently distill representations from vast 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 ESM. |
| title | Lossy Neural Compression for Geospatial Analytics: A Review |
| topic | Signal Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Geophysics |
| url | https://arxiv.org/abs/2503.01505 |