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相关概念视频

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

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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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Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
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一个空间时间数据集用于基于卫星的滑坡检测.

Paul Höhn1,2, Konrad Heidler3, Robert Behling4

  • 1Data Science in Earth Observation, Technical University of Munich (TUM), 80333, Munich, Germany. paul.hoehn@dlr.de.

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概括

一个新的数据集,Sen12Landslides,通过整合多模式,多时间卫星数据来增强山体滑坡监测. 本资源改进了深度学习模型,用于准确地检测山体滑坡和时空异常分析.

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科学领域:

  • 地球观测 地球观测
  • 地缘危险监测监测
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 精确的滑坡检测和监测对于降低风险至关重要.
  • 当前的深度学习模型在卫星图像中与时间动态作斗争,以进行山体滑坡分析.

研究的目的:

  • 介绍Sen12Landslides,这是一个大型的,多模式的,多时间的数据集,用于基于卫星的山体滑坡监测.
  • 在滑坡事件中实现强大的时空异常检测.

主要方法:

  • 开发了Sen12Landslides数据集,包含15个地区的75,000个注释.
  • 综合 Sentinel-1 SAR,Sentinel-2 光学图像,以及哥白尼 DEM 数据.
  • 提供了像素级注释,精确的事件前后时间.

主要成果:

  • 数据集支持先进的深度学习,用于捕获空间和时间滑坡特征.
  • 基准实验显示了模型的实用性,在Sentinel-2数据上,F1得分超过83%.
  • 在不同地区展示了改进的模型培训和通用化.

结论:

  • Sen12山体滑坡有助于更可靠地进行山体滑坡检测和监测.
  • 该数据集在地缘危险评估中推进了地球观测研究.
  • 允许开发更强大的和可泛化的深度学习模型用于山体滑坡分析.