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English(EN) BigEarthNet.txt: A Large-Scale Multi-Sensor Image-Text Dataset and Benchmark for Earth Observation

新的BigEarthNet.txt数据集增强了地球观测VLMs

研究人员推出了BigEarthNet.txt,这是一个旨在改进地球观测任务的视觉语言模型(VLMs)的大规模数据集。该数据集包含超过464,000张配准的Sentinel-1和Sentinel-2图像,并配有960万条文本注释。这些注释涵盖了土地利用/土地覆盖类别、环境背景、视觉问答对以及用于对象检测的指代表达式。评估表明,现有的VLMs在处理遥感数据方面存在困难,但在BigEarthNet.txt上进行微调可显著提高性能。 AI

影响 该数据集有望显著提升AI模型在环境和土地利用应用中分析卫星图像的能力。

排序理由 该项目描述了一个特定AI研究领域(地球观测VLMs)的新数据集和基准。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新的BigEarthNet.txt数据集增强了地球观测VLMs

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该项目描述了一个特定AI研究领域(地球观测VLMs)的新数据集和基准。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Johann-Ludwig Herzog, Mathis J\"urgen Adler, Leonard Hackel, Yan Shu, Angelos Zavras, Ioannis Papoutsis, Paolo Rota, Beg\"um Demir ·

    BigEarthNet.txt:一个用于地球观测的大规模多传感器图像文本数据集和基准

    arXiv:2603.29630v3 Announce Type: replace Abstract: Vision-langugage models (VLMs) have shown strong performance in computer vision (CV), yet their performance on remote sensing (RS) data remains limited due to the lack of large-scale, multi-sensor RS image-text datasets with div…