发表机构
ETHZ（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文构建地理空间基础模型用于全球地上生物量估算的综合基准，发现预计算嵌入产品表现优异，基于AlphaEarth Foundations嵌入训练的模型性能优于当前最优的监督模型。
AI 中文摘要
从卫星影像准确估算地上生物量（AGB）对碳储量的大规模监测至关重要，但在全球尺度上仍是一项极具挑战性的回归任务。地理空间基础模型（GFMs）作为一种从地球观测数据中提取通用表征的机器学习范式已崭露头角，但其在生物量估算等定量回归任务中的效用仍未得到充分探索，因为多数基准侧重分类与分割任务。本文使用AGBD数据集（一个适用于机器学习的基准，涵盖不同生物群落与地理区域），针对全球尺度AGB估算构建了GFMs的综合基准。我们区分了从业者使用GFMs的两种方式：（i）以权重形式分发、由用户运行的模型，我们在PANGAEA基准框架中将其作为冻结编码器进行评估；（ii）以可直接使用的预计算嵌入产品形式分发的模型，我们对AlphaEarth Foundations（AEF）和TESSERA进行了评估。我们将PANGAEA上可用的11种GFMs及这两种嵌入产品与完全监督的当前最优（SOTA）模型进行对比，评估其地理与时间泛化能力，以及与ESA CCI生物量产品在独立参考数据上的一致性。结果显示，作为冻结编码器运行的GFMs性能显著低于监督SOTA模型，而预计算嵌入产品则表现出极高有效性：基于AEF嵌入训练的多层感知机（MLP）性能优于基于AGBD特征训练的监督SOTA模型，而在AEF嵌入（可选搭配选定的原始特征）上训练的同一SOTA模型则取得了最佳整体结果，同时在空间与时间上的泛化能力也更出色。
英文摘要
Accurate estimation of Above-Ground Biomass (AGB) from satellite imagery is essential for the large-scale monitoring of carbon stocks, yet it remains a challenging regression task at global scale. Geospatial Foundation Models (GFMs) have recently emerged as a promising machine learning paradigm to derive general-purpose representations from Earth observation data, but their utility for quantitative regression tasks like biomass estimation remains largely unexplored, as most benchmarks emphasize classification and segmentation. Here, we present a comprehensive benchmark of GFMs for global-scale AGB estimation using the AGBD dataset, a machine learning-ready benchmark spanning diverse biomes and geographies. We distinguish two ways in which GFMs reach practitioners: (i) models distributed as weights to be run by the user, which we evaluate as frozen encoders within the PANGAEA benchmarking framework; and (ii) models distributed as ready-to-use, pre-computed embedding products, for which we evaluate AlphaEarth Foundations (AEF) and TESSERA. We compare 11 GFMs available on PANGAEA and both embedding products against a fully supervised state-of-the-art (SOTA) model, assess their geographical and temporal generalization abilities, as well as agreement with the ESA CCI biomass product on independent reference data. Our results show that GFMs run as frozen encoders substantially underperform with respect to the supervised SOTA model, whereas pre-computed embedding products prove highly effective. An MLP trained on AEF embeddings outperforms the supervised SOTA model trained on AGBD features, and the same SOTA model trained on AEF embeddings (optionally augmented with selected raw features) achieves the best overall result, while also generalizing better across space and time.
