让遥感模型学会识别地块中多种地物的混合比例，提升场景理解能力。
A Composition-Aware Pretraining Framework for Geospatial Foundation Models
- 用像素级地物混合分布作为目标，显式建模复杂场景的组合特性。
- 在零样本图像检索和场景分类上性能超越大模型，相对提升达55.6%。
- 适合需要整体语义理解的任务，如遥感场景分析，不牺牲精细定位能力。
地理空间基础模型已成为地球观测下游任务的领先方法。然而，现有预训练方法多以单一概念处理影像，未能捕捉复杂卫星场景的高度组合性。本文提出一种组合感知预训练框架，显式编码地物混合比例。每张卫星图像的每个像素单元被映射为表示其地物混合分布的直方图，称为“组合目标”。该目标作为主要预测任务，并通过地球移动距离（Earth Mover's Distance）注入骨干网络。实验表明，组合感知预训练在需要语义相似性判断的区域级理解任务中表现显著提升，包括零样本图像检索与场景分类；同时在需要细粒度空间精度的任务（如分割与目标检测）上仍具竞争力。使用3680万参数的骨干网络，本框架在多数检索与分类任务中优于包含3.03亿和6亿参数的SatMAE与Prithvi-EO-2.0。在测试组合判别的ForestNet-12数据集上，基线mAP@10从0.279提升至0.434，相对提升55.6%，直接验证了显式组合建模的有效性。代码见：https://github.com/05kashyap/GFM_Composition_Pretraining。
原文摘要 · Abstract (English)
Geospatial foundation models have emerged as state-of-the-art methods for downstream Earth observation tasks. However, existing pretraining methodologies process imagery through a single-concept lens, failing to capture the highly compositional nature of complex satellite scenes. We propose a composition-aware pretraining framework that explicitly encodes fractional land-cover mixtures. Each satellite image cell is mapped to a histogram representing its fractional land-cover distribution, which we term the "composition target". These targets serve as the primary prediction objective and are distilled into the backbone using Earth Mover's Distance. Experimental evaluation shows that composition-aware pretraining yields substantial gains on region-level understanding tasks requiring semantic similarity judgment, including zero-shot image retrieval and scene classification, while remaining competitive on tasks requiring fine-grained spatial precision, such as segmentation and object detection. With a 36.8M-parameter backbone, our framework outperforms SatMAE and Prithvi-EO-2.0, which contain 303M and 600M parameters, respectively, in most retrieval and scene classification settings. On the fine-grained ForestNet-12 dataset, a rigorous testbed for compositional discrimination, our method boosts baseline mAP@10 from 0.279 to 0.434, a 55.6% relative improvement, providing direct evidence for the effectiveness of explicit composition modeling. The code implementation can be found at https://github.com/05kashyap/GFM_Composition_Pretraining
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