更智能而非更费力的微调：面向地理空间基础模型的参数高效微调
Fine-tune Smarter, Not Harder: Parameter-Efficient Fine-Tuning for Geospatial Foundation Models
AI总结：
针对地理空间基础模型全量微调成本高、易遗忘预训练特征的问题，研究评估多种PEFT技术，证实其性能更优且泛化性更强，并整合进开源包TerraTorch。
摘要：
地球观测（EO）在监测环境变化、灾害响应和自然资源管理中至关重要。在此背景下，基础模型助力遥感图像分析，以准确高效地检索相关地理信息。然而，随着这些模型规模扩大，微调因所需计算资源和成本不断增加而变得愈发困难，限制了其可及性和可扩展性。此外，全量微调可能导致预训练特征被遗忘，甚至降低模型泛化能力。\n为解决这一问题，参数高效微调（PEFT）技术提供了颇具前景的解决方案。本文针对多种基础模型架构和PEFT技术开展了大量实验，在5个不同的EO数据集上评估其效果。研究结果提供了全面对比，揭示了PEFT方法在何时、以何种方式支持预训练地理空间模型的适配。我们证明，PEFT技术的性能可与全量微调持平甚至更优，还能提升模型对未见过的地理区域的泛化能力，同时减少训练时间和内存需求。额外实验探究了解码器类型或元数据使用等架构选择的影响，建议采用UNet解码器且不使用元数据进行微调的配置。我们已将所有评估的基础模型和技术整合到开源包TerraTorch中，以支持快速、可扩展且低成本的模型适配。
英文摘要：
Earth observation (EO) is crucial for monitoring environmental changes, responding to disasters, and managing natural resources. In this context, foundation models facilitate remote sensing image analysis to retrieve relevant geoinformation accurately and efficiently. However, as these models grow in size, fine-tuning becomes increasingly challenging due to the associated computational resources and costs, limiting their accessibility and scalability. Furthermore, full fine-tuning can lead to forgetting pre-trained features and even degrade model generalization. To address this, Parameter-Efficient Fine-Tuning (PEFT) techniques offer a promising solution. In this paper, we conduct extensive experiments with various foundation model architectures and PEFT techniques to evaluate their effectiveness on five different EO datasets. Our results provide a comprehensive comparison, offering insights into when and how PEFT methods support the adaptation of pre-trained geospatial models. We demonstrate that PEFT techniques match or even exceed full fine-tuning performance and enhance model generalisation to unseen geographic regions, while reducing training time and memory requirements. Additional experiments investigate the effect of architecture choices such as the decoder type or the use of metadata, suggesting UNet decoders and fine-tuning without metadata as the recommended configuration. We have integrated all evaluated foundation models and techniques into the open-source package TerraTorch to support quick, scalable, and cost-effective model adaptation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
