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A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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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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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
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相关实验视频

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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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机器学习为玛雅考古学准备的遥感数据.

Žiga Kokalj1, Sašo Džeroski2,3, Ivan Šprajc4

  • 1Research Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU), Novi trg 2, 1000, Ljubljana, Slovenia. ziga.kokalj@zrc-sazu.si.

Scientific data
|August 23, 2023
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概括

这项研究引入了一种新的多式联络注释数据集,用于遥感玛雅考古学,非常适合深度学习. 它帮助研究人员开发计算机视觉模型,以发现古老的玛雅结构.

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科学领域:

  • 考古学的考古学
  • 遥感 遥感 遥感 遥感
  • 计算机视觉 计算机视觉

背景情况:

  • 玛雅文明留下了大量的考古记录.
  • 遥感技术为研究这些遗址提供了新的途径.
  • 开发人工智能模型需要全面的,注释的数据集.

研究的目的:

  • 为玛雅考古学创建一个多模式注释数据集.
  • 为了促进深度学习应用在现场.
  • 支持开发用于考古研究的计算机视觉模型.

主要方法:

  • 收集的空中激光扫描 (ALS) 数据 (可视化,天花板高度模型).
  • 获取了"哨兵一号"和"哨兵二号"卫星图像.
  • 作为二进制面具,对玛雅结构 (建筑物,平台,aguadas) 进行手动注释.

主要成果:

  • 一个全面的数据集,涵盖了位于尤卡坦半岛的查克地区.
  • 数据集包括五种数据类型:ALS,Sentinel-1,Sentinel-2,以及手动注释.
  • 注释精确地描绘了古玛雅结构的位置和边界.

结论:

  • 数据集已经准备好用于机器学习任务,例如对象识别和语义细分.
  • 这个资源将使研究团队能够为玛雅考古学建立或增强计算机视觉模型.
  • 为考古发现提供远程传感数据的高级分析.