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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Updated: Feb 28, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

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使用无人机图像和深度学习对象检测模型的年轻白松检测.

Abishek Poudel1, Eddie Bevilacqua1

  • 1Department of Sustainable Resources Management, State University of New York College of Environmental Science and Forestry, Syracuse, NY 13210, USA.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
概括
此摘要是机器生成的。

无人机 (UAV) 图像与深度学习 (DL) 结合,准确地监测白松再生. 这种高效的方法减少了手动森林评估的需求.

关键词:
F-RCNNN 在线阅读混矩阵是一个混矩阵.森林监测是指对森林进行监测.复兴再生是一种再生方式.

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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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相关实验视频

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科学领域:

  • 林业林业 林业 林业 林业
  • 遥感 遥感 遥感 遥感
  • 人工智能的人工智能

背景情况:

  • 森林再生监测对于可持续森林管理至关重要.
  • 评估年轻树的传统野外工作是劳动密集型和耗时的.
  • 整合先进技术可以提高森林监测的效率和准确性.

研究的目的:

  • 评估将无人机 (UAV) 图像和深度学习 (DL) 结合起来,以监测白松 (Pinus strobus) 再生的有效性.
  • 为了比较各种DL物体检测模型的性能,用于识别和评估白松苗.
  • 为了确定最佳的UAV-DL系统配置,用于运营森林再生评估.

主要方法:

  • 使用无人机飞行获取高分辨率RGB和多光谱正方体.
  • 在ArcGIS Pro.中评估了20个深度学习对象检测模型.
  • 在纽约圣劳伦斯县的不同白松大小类别和密度中测试模型性能.
  • 使用混矩阵分析来确定整体准确性.

主要成果:

  • 更快的R-CNN (F-RCNN) 模型表现出卓越的性能,平均精度为0.88.
  • 在中密度和高密度的白松树木中,F-RCNN模型的整体准确率分别为91%和90%.
  • 在RGB和多光谱图像类型中,模型性能一致.

结论:

  • 基于无人机的深度学习系统为监测白松再生提供了准确有效的方法.
  • 这项技术大大减少了大量手工现场工作的需要.
  • 这些发现支持UAV-DL用于森林再生评估的操作实施.