为遥感神经嵌入设计标准化评估框架，支持高效压缩与多任务应用。
NeuCo-Bench: A Novel Benchmark Framework for Neural Embeddings in Earth Observation
- 基于固定大小嵌入构建评估流水线，实现任务无关的紧凑表征。
- 引入隐藏任务排行榜，减少预训练偏差，提升评估公平性。
- 适合遥感、地球观测等领域研究者，推动模型可比性与复现性。
我们提出NeuCo-Bench，一个面向地球观测（EO）领域神经压缩与表征学习的新型基准评估框架。该框架基于固定尺寸嵌入，提供紧凑且适用于多种下游任务的通用表征。NeuCo-Bench包含三个部分：(i) 以嵌入为核心的评估流程；(ii) 采用隐藏任务排行榜的挑战模式，降低预训练偏见；(iii) 平衡精度与稳定性的评分机制。为保障可复现性，我们发布了SSL4EO-S12-downstream数据集，该数据集为经过筛选的多光谱、多时相遥感数据。我们在2025年CVPR EARTHVISION研讨会举办了公开挑战赛，并对主流基础模型进行了消融实验。NeuCo-Bench推动了遥感领域神经嵌入的社区共建与标准化评估，具有推广至其他领域的潜力。
原文摘要 · Abstract (English)
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.
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