标题： 通过基础模型组合实现可扩展和通用的地球观测数据挖掘 显示英文标题

标题： Towards Scalable and Generalizable Earth Observation Data Mining via Foundation Model Composition

摘要： 基础模型正在迅速改变地球观测数据挖掘，通过为场景分类和语义分割等关键任务提供可泛化和可扩展的解决方案。 尽管地理空间领域大多数努力都集中在使用大量地球观测数据集从头开始训练大型模型，但一种尚未得到充分探索的替代策略是重用和组合现有的预训练模型。 在本研究中，我们调查了在遥感和通用视觉数据集上预训练的基础模型是否可以有效结合，以提高一系列关键地球观测任务的性能。 使用GEO-Bench基准，我们在覆盖多种空间分辨率、传感器模态和任务类型的十一个数据集上评估了几种著名模型，包括Prithvi、Hiera和DOFA。 结果表明，较小的预训练模型的特征级集成可以达到或超过更大模型的性能，同时需要更少的训练时间和计算资源。 此外，该研究突出了应用知识蒸馏将集成的优势转移到更紧凑模型中的潜力，为在实际地球观测应用中部署基础模型提供了可行的路径。 显示英文摘要

摘要： Foundation models are rapidly transforming Earth Observation data mining by enabling generalizable and scalable solutions for key tasks such as scene classification and semantic segmentation. While most efforts in the geospatial domain have focused on developing large models trained from scratch using massive Earth Observation datasets, an alternative strategy that remains underexplored is the reuse and combination of existing pretrained models. In this study, we investigate whether foundation models pretrained on remote sensing and general vision datasets can be effectively combined to improve performance across a diverse set of key Earth Observation tasks. Using the GEO-Bench benchmark, we evaluate several prominent models, including Prithvi, Hiera, and DOFA, on eleven datasets covering a range of spatial resolutions, sensor modalities, and task types. The results show that feature-level ensembling of smaller pretrained models can match or exceed the performance of much larger models, while requiring less training time and computational resources. Moreover, the study highlights the potential of applying knowledge distillation to transfer the strengths of ensembles into more compact models, offering a practical path for deploying foundation models in real-world Earth Observation applications.