GeoSANE：从模型而非数据中学习地理空间表征
GeoSANE: Learning Geospatial Representations from Models, Not Data
把「预训练一个模型」换成「从已有模型权重生成新模型权重」，为视觉研究者提供了一条不依赖海量数据、直接复用多个基础模型互补知识的迁移路径，尤其适合遥感等标注稀缺、模态多样的场景下快速产出可微调骨干。
原文依据 learns a unified neural representation from the weights of existing foundation models and task-specific models
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论遥感领域近期的进展使得可用的基础模型数量不断增加；这些模型各自在不同的模态、数据集和目标上训练，却只捕捉到庞大地理空间知识版图中的一部分。尽管这些模型在各自领域内表现强劲，其能力仍是互补的而非统一的。因此，我们并不打算在多个模型之间做出取舍，而是希望将其优势整合进一个共享表征之中。我们提出 GeoSANE，一个地理空间模型铸造厂，它从现有基础模型与任务专用模型的权重中学习统一的神经表征，能够按需生成新的神经网络权重。给定目标架构，GeoSANE 即可生成可直接用于微调的权重，服务于跨多种模态的分类、分割与检测任务。由 GeoSANE 生成的模型一致优于从零训练的对应模型，达到或超越最优的遥感基础模型，并在生成轻量化网络时优于通过剪枝或知识蒸馏得到的模型。在十个多样化数据集以及 GEO-Bench 上的评测证实了其强大的泛化能力。通过从预训练转向权重生成，GeoSANE 引入了一个跨模型、跨任务统一与迁移地理空间知识的新框架。代码见 https://hsg-aiml.github.io/GeoSANE/ 。
英文原文摘要 1506 字符
Recent advances in remote sensing have led to an increase in the number of available foundation models; each trained on different modalities, datasets, and objectives, yet capturing only part of the vast geospatial knowledge landscape. While these models show strong results within their respective domains, their capabilities remain complementary rather than unified. Therefore, instead of choosing one model over another, we aim to combine their strengths into a single shared representation.We introduce GeoSANE, a geospatial model foundry that learns a unified neural representation from the weights of existing foundation models and task-specific models, able to generate novel neural networks weights on-demand. Given a target architecture, GeoSANE generates weights ready for finetuning for classification, segmentation, and detection tasks across multiple modalities.Models generated by GeoSANE consistently outperform their counterparts trained from scratch, match or surpass state-of-the-art remote sensing foundation models, and outperform models obtained through pruning or knowledge distillation when generating lightweight networks. Evaluations across ten diverse datasets and on GEO-Bench confirm its strong generalization capabilities.By shifting from pre-training to weight generation, GeoSANE introduces a new framework for unifying and transferring geospatial knowledge across models and tasks. Code is available at \href https://hsg-aiml.github.io/GeoSANE/ hsg-aiml.github.io/GeoSANE/ .