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The term "intelligence" is complex because it refers to both behavior and individuals, and its interpretation varies across cultures. European Americans tend to link intelligence with reasoning and cognitive skills, while in Kenya, it is tied to responsible participation in family and social life. In Uganda, intelligence is seen as the ability to know the right actions and carry them out effectively, while the Iatmul people of Papua New Guinea associate it with the capacity to remember...
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Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
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人工智能和陆地点云用于森林监测

Maksymilian Kulicki1,2, Carlos Cabo3, Tomasz Trzciński1,4,5

  • 1IDEAS NCBR, ul. Chmielna 69, 00-801 Warsaw, Poland.

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概括

人工智能 (AI),特别是深度学习 (DL),通过LiDAR数据显著改善了森林监测. 林业人工智能的进步需要基准数据集和开放的数据共享,以便进行可复制的研究.

关键词:
深度学习是一种深度学习.森林库存 森林库存 森林库存李达尔 (LiDAR) 是一种激光雷达.机器学习 机器学习开放数据是开放的数据.精准林业是一门精准的林业.在 TLS 中使用 TLS.树的特征 树的特征

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科学领域:

  • 林业科学 林业科学
  • 计算机科学 计算机科学
  • 遥感 遥感 遥感 遥感

背景情况:

  • 基于地面的LiDAR点云提供详细的3D森林数据.
  • 传统的方法难以应对森林库存LiDAR数据的复杂性.
  • 人工智能 (AI),特别是深度学习 (DL),显示出分析这些数据的前景.

研究的目的:

  • 审查人工智能和DL与地面LiDAR用于森林监测的整合.
  • 识别当前的趋势,进展和未来的研究方向.
  • 突出AI在加强森林管理和保护方面的潜力.

主要方法:

  • 关于AI/DL应用在陆地LiDAR数据分析中的最新研究的综述.
  • 专注于诸如语义细分,个体树细分和物种分类等技术.
  • 讨论挑战和提出的解决方案,包括基准数据集和合成数据.

主要成果:

  • 在使用LiDAR的森林清单任务中,DL模型的性能优于传统的机器学习.
  • 在植被结构标记,树木细分和物种分类方面的关键进展.
  • 挑战包括缺乏标准化指标,数据/代码共享,以及对参考数据的需求.

结论:

  • 人工智能,特别是DL,对于使用LiDAR进行准确和高效的森林监测具有变革性.
  • 关键需求包括基准数据集,开放访问政策和探索新型DL架构.
  • 这些进展对于可复制性,比较研究和改善森林管理至关重要.