研究如何将像素嵌入聚合为区域表示，提升地理空间模型在分布偏移下的泛化能力。
From Pixels to Patches: Pooling Strategies for Earth Embeddings
- 提出三类无需训练的池化策略，以更好保留局部空间差异
- 统计池化使地理分布测试准确率提升最高6%，误差减少超50%
- 推荐分层使用均值、统计量与协方差池化，兼顾效率与精度
地理空间基础模型日益提供可下载的像素级嵌入，下游任务若使用区域标签，则需对密集像素嵌入进行后处理聚合。默认的均值池化会忽略块内变化，在空间分布偏移下表现不佳。为此，我们构建了EuroSAT-Embed数据集，包含81,000个来自AlphaEarth、OlmoEarth和Tessera三个模型的嵌入GeoTIFF文件。基于这些固定嵌入，我们在随机与地理不重叠测试划分下，评估11种免训练池化方法及2种基于训练集拟合的基线。结果表明，更丰富的池化方案相比均值池化可将地理泛化差距缩小超过50%，并在空间分割上最高提升6%准确率。建议采用三层策略：(1) 均值作为基准；(2) 统计池化（最小/最大/均值/标准差）作为默认方案，维度扩展4倍；(3) 协方差池化用于追求最佳性能。在所有三类嵌入产品中，简单分布统计均优于均值池化。
原文摘要 · Abstract (English)
Geospatial foundation models increasingly expose pixel-level embedding products that can be downloaded and reused without access to the underlying encoder. In this setting, downstream tasks with patch- or region-level labels require a post-hoc aggregation step that maps dense pixel embeddings to a single representation. The default choice, mean pooling, discards within-patch variability and can underperform under spatial distribution shift. To study this setting, we introduce EuroSAT-Embed: 81,000 embedding GeoTIFFs derived from three foundation models: AlphaEarth, OlmoEarth, and Tessera. Using these fixed embedding products, we benchmark 11 training-free pooling methods and 2 train-set-fitted baselines under both random and geographically disjoint test splits. Richer pooling schemes reduce the geographic generalization gap by over 50% relative to mean pooling and improve accuracy by up to 6% on spatial splits. We recommend a three-tier strategy: (1) mean as a baseline, (2) stats pooling (min/max/mean/std) as the default at 4x the embedding dimension, and (3) covariance pooling for peak accuracy. Across all three embedding products, simple distributional statistics improve spatial-split performance over mean pooling.
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