发表机构
University of Maryland, Baltimore County; NASA Goddard Space Flight Center（马里兰大学巴尔的摩分校; 美国国家航空航天局戈达德太空飞行中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出SPECTRA框架，通过波段路由嵌入（BRE）解决地理空间基础模型的光谱不匹配问题，利用分阶段可迁移性感知LoRA（ST-LoRA）降低微调成本，在多数据集上验证了方法的有效性。
AI 中文摘要
地理空间基础模型（GeoFMs）在地球观测（EO）、气候、天气等大规模地理空间数据上进行预训练后，在各类下游任务微调中展现出良好性能。但将EO预训练的GeoFMs适配实际下游数据集存在两大挑战：一是如何处理光谱不匹配问题——预训练的补丁嵌入需要固定的输入波段集合，而下游传感器可能提供不同的通道；二是如何降低微调成本并提升效率。现有研究虽分别针对这些挑战开展工作，但在光谱不匹配下同时提升微调性能并降低适配成本的联合改进仍未被充分探索。本文提出SPECTRA，这是一种参数高效的微调框架，可同时解决光谱不匹配与适配成本问题。为处理光谱不匹配，SPECTRA引入波段路由嵌入（BRE），将下游所有可用波段映射到预训练GeoFM所需的波段空间，通过BRE可利用下游数据集中的所有可用波段改进所选波段的输入，且无需改变预训练补丁嵌入接口。为降低适配成本，SPECTRA进一步引入分阶段可迁移性感知LoRA（ST-LoRA）微调，ST-LoRA在微调前估计分阶段可迁移性，并为各阶段分配特定LoRA秩，将可训练参数集中在目标任务可迁移性高的阶段。在3种EO预训练GeoFMs和4个下游分割数据集上的实验表明，BRE通过利用所有光谱波段提升了性能，而ST-LoRA与全微调及标准LoRA相比减少了可训练参数。代码可在该https URL获取。
英文摘要
Geospatial foundation models (GeoFMs), pretrained on large-scale geospatial data such as Earth observation (EO), climate, and weather data, have shown promising performance when fine-tuned on diverse downstream tasks. However, there are two challenges of adapting EO-pretrained GeoFMs to practical downstream datasets. The first challenge is how to handle spectral mismatch: pretrained patch embeddings expect a fixed set of input bands, whereas downstream sensors may provide different channels. The second challenge is how to reduce fine-tuning cost and make it efficient. While existing work has made efforts on these challenges individually, jointly improving fine-tuning performance under spectral mismatch while reducing adaptation cost remains underexplored. We propose SPECTRA, a parameter-efficient fine-tuning framework that addresses both spectral mismatch and adaptation cost. To handle spectral mismatch, SPECTRA introduces Band-Routed Embedding (BRE), which maps all available downstream bands into the band space expected by the pretrained GeoFM. By using BRE, all available bands in the downstream dataset are utilized to improve the selected-band input without changing the pretrained patch embedding interface. To reduce adaptation cost, SPECTRA further introduces a Stage-wise Transferability-aware LoRA (ST-LoRA) fine-tuning. ST-LoRA estimates stage-wise transferability before fine-tuning and assigns stage-specific LoRA ranks, concentrating trainable parameters on the stages with high transferability for the target task. Across three EO-pretrained GeoFMs and four downstream segmentation datasets, experiments show that BRE improves performance by utilizing all spectral bands, while ST-LoRA reduces trainable parameters compared with full fine-tuning and standard LoRA. Code is available at https://github.com/big-data-lab-umbc/SPECTRA.
CommentsAccepted at ACM SIGSPATIAL 2026 Research track. Updated to the camera-ready version
