野火过火区制图对于灾损评估、排放建模以及理解不同生态区域中火灾与气候的相互作用至关重要。近期提出的地理空间基础模型（Geospatial Foundation Models, GFMs）为卫星影像提供了强大的通用表征能力，但目前尚缺乏关于如何高效地将此类模型适配至下游地球观测任务的明确共识，尤其在面临地理与时间域偏移（geographic and temporal domain shift）时。本研究评估了三种前沿地理空间基础模型——Terramind、DINOv3 和 Prithvi-v2——在使用 Sentinel-2 数据开展美国与加拿大全域野火过火区制图任务中的性能。基于 2017–2023 年间 3,820 起野火事件，我们在多种生物群落中开展了空间与时间泛化性测试。我们系统比较了全模型微调（full fine-tuning）、仅解码器微调（decoder-only fine-tuning）及低秩自适应（Low-Rank Adaptation, LoRA）三种适配策略。所有实验结果表明，LoRA 在跨域泛化性能上表现最优，且仅需更新不足 1% 的参数，展现出精度与效率之间的良好权衡。其中，采用 LoRA 适配的 Prithvi-v2 取得了最高的整体精度，并相较全模型微调实现了最大幅度的性能提升。上述发现表明，结合 LoRA 等轻量级参数高效适配方法的地理空间基础模型，可为大规模野火过火区制图提供一种鲁棒且可扩展的解决方案。代码开源地址：https://github.com/alishibli97/wildfire-lora-gfm。

Wildfire burned-area mapping is essential for damage assessment, emissions modeling, and understanding fire-climate interactions across diverse ecological regions. Recent geospatial foundation models provide strong general-purpose representations for satellite imagery, yet there is still no clear understanding of how to efficiently adapt these models for downstream Earth observation tasks, particularly under geographic and temporal domain shift. This study evaluates three state-of-the-art Geospatial Foundation Models (GFMs) - Terramind, DINOv3, and Prithvi-v2 - for burned-area mapping across the United States and Canada using Sentinel-2 data. Leveraging 3,820 wildfire events from 2017-2023, we conduct spatial and temporal generalization tests across diverse biomes. We systematically compare full fine-tuning, decoder-only fine-tuning, and Low-Rank Adaptation (LoRA) for adapting each model. Across all experiments, LoRA provides the strongest cross-domain generalization while updating less than 1% of parameters, demonstrating a favorable trade-off between accuracy and efficiency. Prithvi-v2 with LoRA achieves the highest overall accuracy and the largest improvement compared to full fine-tuning. These findings indicate that geospatial foundation models, when adapted using lightweight parameter-efficient methods such as LoRA, offer a robust and scalable solution for large-scale burned-area mapping. Code is available at https://github.com/alishibli97/wildfire-lora-gfm.