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相关概念视频

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相关实验视频

Updated: Jan 13, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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城市树木健康和生态系统服务的轻量级多式融合.

Abror Buriboev1, Djamshid Sultanov2, Ilhom Rahmatullaev3,4

  • 1Department of AI-Software, Gachon University, Sujeong-Gu, Seongnam-si 13120, Republic of Korea.

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概括

本研究引入了一种轻量级的深度学习模型,用于实时评估城市树木健康状况. 该框架准确地监测树木的状况,并使用化传感器数据估计氧气和二氧化碳的交换.

关键词:
碳封存估计的估计.边缘高效的人工智能环境情报环境情报图像 传感器集成多任务神经网络多任务神经网络多式联络融合多式联络融合氧气生产建模模型监测树木健康状况 监测树木健康状况城市生态学 城市生态学

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科学领域:

  • 环境科学 环境科学
  • 计算机科学 计算机科学
  • 城市生态城市生态学

背景情况:

  • 城市扩张需要先进的方法来监测树木健康和生态系统服务.
  • 目前的手动树木检查是主观和低效的.
  • 城市树木对于空气质量和气候调节至关重要.

研究的目的:

  • 开发一个可扩展的实时框架来评估城市树木的健康状况.
  • 估计城市树木每天的氧气生产和二氧化碳吸收.
  • 为了克服手动树木监测的局限性.

主要方法:

  • 一个轻量级的多式联网深度学习框架,将RGB图像与环境和生物识别传感器数据融合在一起.
  • 使用一个EfficientNet-B0视觉编码器与MBConv和挤压和激发注意力.
  • 实施了三项任务学习设置,用于同时进行分类和回归.

主要成果:

  • 在树木健康分类中获得了92.03%的准确性.
  • 减少了O2 (MAE = 1.28) 和CO2 (MAE = 1.70) 的回归误差.
  • 与单模和多模基线相比,表现出优越的性能.

结论:

  • 拟议的多式联运框架提供了准确和高效的城市树木健康评估.
  • 该模型适合在边缘设备上部署,用于实时监控.
  • 这种方法提高了对城市生态系统服务的理解和管理.