GeoMeld：迈向遥感领域语义引导的基模模型
GeoMeld: Toward Semantically Grounded Foundation Models for Remote Sensing
AI总结：
本文提出GeoMeld数据集，通过语义引导的监督方法，结合多模态对齐，提升遥感领域基模模型的性能和鲁棒性。
摘要：
有效的遥感基础建模需要空间对齐的异构模态与语义引导的监督，但此类资源在大规模上仍有限。我们提出了GeoMeld，一个包含约250万空间对齐样本的大规模多模态数据集。该数据集涵盖多样化的模态和分辨率，并在统一的对齐协议下构建，用于模态感知的表示学习。GeoMeld通过代理标注框架提供语义引导的语言监督，该框架合成并验证来自光谱信号、地形统计和结构化地理元数据的注释，编码可测量的跨模态关系。为了利用该数据集，我们引入GeoMeld-FM预训练框架，结合多任务掩码自动编码、JEPA表示学习和标注-视觉对比对齐。联合目标使学习的表示空间能够捕捉可靠的跨传感器物理一致性和语义。实验表明在下游迁移和跨传感器鲁棒性上均取得一致提升。GeoMeld和GeoMeld-FM共同建立了一个可扩展的参考框架，用于遥感领域语义引导的多模态基础建模。
英文摘要：
Effective foundation modeling in remote sensing requires spatially aligned heterogeneous modalities coupled with semantically grounded supervision, yet such resources remain limited at scale. We present GeoMeld, a large-scale multimodal dataset with approximately 2.5 million spatially aligned samples. The dataset spans diverse modalities and resolutions and is constructed under a unified alignment protocol for modality-aware representation learning. GeoMeld provides semantically grounded language supervision through an agentic captioning framework that synthesizes and verifies annotations from spectral signals, terrain statistics, and structured geographic metadata, encoding measurable cross-modality relationships within textual descriptions. To leverage this dataset, we introduce GeoMeld-FM, a pretraining framework that combines multi-pretext masked autoencoding over aligned modalities, JEPA representation learning, and caption-vision contrastive alignment. This joint objective enables the learned representation space to capture both reliable cross-sensor physical consistency and grounded semantics. Experiments demonstrate consistent gains in downstream transfer and cross-sensor robustness. Together, GeoMeld and GeoMeld-FM establish a scalable reference framework for semantically grounded multi-modal foundation modeling in remote sensing.
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