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English(EN) Looks Can be Deceiving: Annotator and Reviewer Performance Across Imagery Sources in Crowd-Sourced Aerial Damage Assessment

论文发现人类损害评估准确性因影像源而异

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新论文调查了人类标注员和审核员在使用无人机、载人飞机和卫星等不同来源的航空影像评估损害时的表现。研究发现，初次标注需要最终委员会进行大量修订，其中卫星影像等低分辨率源需要最多的修正。即使经过一次审核，仍然存在显著分歧，尤其是在卫星数据方面，这表明对于多源数据集而言，统一的审核分配可能不是最优的。 AI

影响 凸显了AI模型数据标注的挑战，特别是关于用于训练的不同影像源的可靠性。

排序理由 学术论文发表在arXiv上 [lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

论文发现人类损害评估准确性因影像源而异

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学术论文发表在arXiv上 [lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Thomas Manzini, Priyankari Perali, Raisa Karnik, Stephen Johnson, Robin R. Murphy ·

    外表可能具有欺骗性：众包航拍灾害评估中不同图像来源的标注员和审查员表现

    arXiv:2608.14942v1 Announce Type: cross Abstract: This paper presents the first known empirical investigation of annotator and reviewer performance across multi-source remotely sensed imagery, evaluating human labeling across drone, crewed aviation, and satellite views. Because e…

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