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English(EN) TerraMind: Large-Scale Generative Multimodality for Earth Observation

TerraMind:首个面向地球观测的任意到任意多模态基础模型

研究人员推出了TerraMind,一个专为地球观测任务设计的新型多模态基础模型。该模型独特地结合了令牌级和像素级数据表示,使其能够捕捉高级上下文信息和细粒度的空间细节。TerraMind展示了强大的零样本和少样本学习能力,引入了一种称为“模态内思考”(TiM)的新技术,用于微调和推理期间的数据增强,并在PANGAEA等基准测试中取得了最先进的性能。该模型、其预训练数据集以及相关代码均在宽松许可下公开提供。 AI

影响 引入了一个面向地球观测的新型多模态基础模型,有望在地理空间数据分析和应用方面提升能力。

排序理由 该集群描述了一篇介绍新型AI模型和技术的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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TerraMind:首个面向地球观测的任意到任意多模态基础模型

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该集群描述了一篇介绍新型AI模型和技术的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Johannes Jakubik, Felix Yang, Benedikt Blumenstiel, Erik Scheurer, Rocco Sedona, Stefano Maurogiovanni, Jente Bosmans, Nikolaos Dionelis, Valerio Marsocci, Niklas Kopp, Rahul Ramachandran, Paolo Fraccaro, Thomas Brunschwiler, Gabriele Cavallaro, Juan Ber… ·

    TerraMind:地球观测的大规模生成式多模态

    arXiv:2504.11171v5 Announce Type: replace-cross Abstract: We present TerraMind, the first any-to-any generative, multimodal foundation model for Earth observation (EO). Unlike other multimodal models, TerraMind is pretrained on dual-scale representations combining both token-leve…