NeuCo-Bench: A Novel Benchmark Framework for Neural Embeddings in Earth Observation
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910051225042944 |
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| author | Vinge, Rikard Wittmann, Isabelle Schneider, Jannik Marszalek, Michael Gilch, Luis Brunschwiler, Thomas Albrecht, Conrad M |
| author_facet | Vinge, Rikard Wittmann, Isabelle Schneider, Jannik Marszalek, Michael Gilch, Luis Brunschwiler, Thomas Albrecht, Conrad M |
| contents | We introduce NeuCo-Bench, a novel benchmark framework for evaluating (lossy) neural compression and representation learning in the context of Earth Observation (EO). Our approach builds on fixed-size embeddings that act as compact, task-agnostic representations applicable to a broad range of downstream tasks. NeuCo-Bench comprises three components: (i) an evaluation pipeline built around embeddings, (ii) a challenge mode with a hidden-task leaderboard designed to mitigate pretraining bias, and (iii) a scoring system that balances accuracy and stability. To support reproducibility, we release SSL4EO-S12-downstream, a curated multispectral, multitemporal EO dataset. We present results from a public challenge at the 2025 CVPR EARTHVISION workshop and conduct ablations with state-of-the-art foundation models. NeuCo-Bench provides a step towards community-driven, standardized evaluation of neural embeddings for EO and beyond. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17914 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NeuCo-Bench: A Novel Benchmark Framework for Neural Embeddings in Earth Observation Vinge, Rikard Wittmann, Isabelle Schneider, Jannik Marszalek, Michael Gilch, Luis Brunschwiler, Thomas Albrecht, Conrad M Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition We introduce NeuCo-Bench, a novel benchmark framework for evaluating (lossy) neural compression and representation learning in the context of Earth Observation (EO). Our approach builds on fixed-size embeddings that act as compact, task-agnostic representations applicable to a broad range of downstream tasks. NeuCo-Bench comprises three components: (i) an evaluation pipeline built around embeddings, (ii) a challenge mode with a hidden-task leaderboard designed to mitigate pretraining bias, and (iii) a scoring system that balances accuracy and stability. To support reproducibility, we release SSL4EO-S12-downstream, a curated multispectral, multitemporal EO dataset. We present results from a public challenge at the 2025 CVPR EARTHVISION workshop and conduct ablations with state-of-the-art foundation models. NeuCo-Bench provides a step towards community-driven, standardized evaluation of neural embeddings for EO and beyond. |
| title | NeuCo-Bench: A Novel Benchmark Framework for Neural Embeddings in Earth Observation |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.17914 |