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相关概念视频

Topographic Surveying and Contours01:29

Topographic Surveying and Contours

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Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
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Field Application of Global Positioning System01:28

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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Reducing Line Loss01:18

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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相关实验视频

Updated: Feb 28, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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树分网 (TreeSeg-Net):一个端到端的实例分段网络,用于树叶脱落的森林点云,使用全球背景和空间近距离.

Xingmei Xu1, Ruihang Zhang1, Shunfu Xiao2

  • 1College of Information Technology, Jilin Agricultural University, Changchun 130118, China.

Plants (Basel, Switzerland)
|February 27, 2026
PubMed
概括

本研究介绍了TreeSeg-Net,这是一个先进的深度学习模型,用于精确地从复杂的森林点云中细分单个树木. 它提高了森林库存的准确性,特别是在脱叶季节.

关键词:
树木Seg-Net 树木区分网无人机摄影测量仪在无人机上使用.点云细分 分点云细分精密林业是指精确的林业.远程传感是一种遥感技术.

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科学领域:

  • 林业科学 林业科学
  • 计算机视觉 计算机视觉 计算机视觉
  • 遥感是一种远程传感.

背景情况:

  • 森林生态系统对于碳循环和生物多样性至关重要.
  • 无人驾驶飞行器 (UAV) 技术提供了具有成本效益的森林数据.
  • 复杂森林中的脱落状况,由于交织的树冠和模糊的边界,造成了细分挑战.

研究的目的:

  • 开发一种自动化方法,从无人机衍生的点云中精确地分类单个树.
  • 为了解决复杂的现有细分技术的局限性,叶子脱落的森林环境.
  • 通过精确的树参数提取来改善森林资源管理.

主要方法:

  • 提出了TreeSeg-Net,这是一个用于原始点云的端到端实例细分网络.
  • 整合了一个全球上下文注意模块 (GCAM) 来捕捉远程依赖.
  • 引入了一个具有几何约束的空间近距离权重模块 (SPWM),以减少低细分.

主要成果:

  • 树木Seg-Net实现了97.2%的平均精度 (AP),例如分段.
  • 该网络在语义细分方面实现了99.7%的平均交叉与联盟 (mIoU).
  • 与主流细分网络相比,证明了更高的准确性.

结论:

  • 树木Seg-Net提供了一个高效和自动化的解决方案,用于精确的森林资源库存.
  • 该方法有效地处理复杂的森林结构和细分挑战.
  • 能够直接从点云数据中准确地分离单个树.