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GHSL:2018 年全球建筑物高度 (P2023A)
此空间栅格数据集描绘了 2018 年建筑高度的全球分布情况,分辨率为 100 米。用于预测建筑高度的输入数据包括 ALOS 全球数字地表模型 (30 米)、NASA 航天飞机雷达地形测绘任务… alos building built built-environment builtup copernicus -
GHSL:全球建成区面积 10 米 (P2023A)
此光栅数据集描绘了 2018 年根据 S2 图像数据观察到的已建成地表的空间分布情况(以每 10 米网格单元格的平方米数表示)。这些数据集衡量以下指标:a) 总建筑物表面面积,以及 b) 分配给网格单元的建筑物表面面积… built built-environment builtup copernicus ghsl jrc -
GHSL:1975-2030 年全球建成地表 (P2023A)
此栅格数据集描绘了建成区地表的分布情况,以每 100 米网格单元面积(平方米)为单位。该数据集衡量了:a) 总建筑物表面面积,以及 b) 分配给主要用于非住宅用途 (NRES) 的网格单元的建筑物表面面积。数据在空间和时间上经过插值或… built built-environment builtup copernicus ghsl jrc -
GHSL:2018 年全球结算特征(10 米)(P2023A)
此空间栅格数据集以 10 米的分辨率描绘了人类聚落,并从建筑环境的功能和高度相关组件方面描述了它们的内部特征。如需详细了解 GHSL 数据产品,请参阅 2023 年 GHSL 数据包报告… 建筑物 建成 建成区 哥白尼 ghsl 高度 -
Tsinghua FROM-GLC 改为不透水表面的年份
此数据集包含 1985 年至 2018 年全球不透水表面面积的年变化信息,分辨率为 30 米。我们采用监督分类和时间一致性检查相结合的方法,确定了从透水性地表变为不透水性地表的变化。不透水像素是指不透水面积占比超过 50% 的像素。… 建成 人口 清华 城市
Datasets tagged built in Earth Engine
[null,null,[],[[["\u003cp\u003eThe Global Human Settlement Layer (GHSL) provides datasets characterizing human settlements, including building heights and built-up surfaces, at resolutions ranging from 10m to 100m.\u003c/p\u003e\n"],["\u003cp\u003eGHSL data utilizes various sources like ALOS, SRTM, and Sentinel-2 imagery to model built environments and their functional components.\u003c/p\u003e\n"],["\u003cp\u003eBuilt-up surface datasets within GHSL offer insights into total and non-residential built areas, spanning multiple years and resolutions.\u003c/p\u003e\n"],["\u003cp\u003eThe Tsinghua FROM-GLC dataset provides insights into annual impervious surface changes globally from 1985 to 2018 at a 30m resolution.\u003c/p\u003e\n"]]],[],null,["# Datasets tagged built in Earth Engine\n\n-\n\n |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### GHSL: Global building height 2018 (P2023A)](/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_H) |\n | This spatial raster dataset depicts the global distribution of building heights at a resolution of 100 m, referred to the year 2018. The input data used to predict building heights are the ALOS Global Digital Surface Model (30 m), the NASA Shuttle Radar Topographic Mission ... |\n | [alos](/earth-engine/datasets/tags/alos) [building](/earth-engine/datasets/tags/building) [built](/earth-engine/datasets/tags/built) [built-environment](/earth-engine/datasets/tags/built-environment) [builtup](/earth-engine/datasets/tags/builtup) [copernicus](/earth-engine/datasets/tags/copernicus) |\n\n-\n\n |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### GHSL: Global built-up surface 10m (P2023A)](/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_S_10m) |\n | This raster dataset depicts the distribution of built-up surfaces, expressed in square metres per 10 m grid cell, for 2018 as observed from the S2 image data. The datasets measure: a) the total built-up surface, and b) the built-up surface allocated to grid cells of ... |\n | [built](/earth-engine/datasets/tags/built) [built-environment](/earth-engine/datasets/tags/built-environment) [builtup](/earth-engine/datasets/tags/builtup) [copernicus](/earth-engine/datasets/tags/copernicus) [ghsl](/earth-engine/datasets/tags/ghsl) [jrc](/earth-engine/datasets/tags/jrc) |\n\n-\n\n |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### GHSL: Global built-up surface 1975-2030 (P2023A)](/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_S) |\n | This raster dataset depicts the distribution of built-up surfaces, expressed in square metres per 100 m grid cell. The dataset measures: a) the total built-up surface, and b) the built-up surface allocated to grid cells of predominant non-residential (NRES) use. Data are spatially-temporally interpolated or ... |\n | [built](/earth-engine/datasets/tags/built) [built-environment](/earth-engine/datasets/tags/built-environment) [builtup](/earth-engine/datasets/tags/builtup) [copernicus](/earth-engine/datasets/tags/copernicus) [ghsl](/earth-engine/datasets/tags/ghsl) [jrc](/earth-engine/datasets/tags/jrc) |\n\n-\n\n |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### GHSL: Global settlement characteristics (10 m) 2018 (P2023A)](/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_C) |\n | This spatial raster dataset delineates human settlements at 10 m resolution, and describes their inner characteristics in terms of the functional and height-related components of the built environment. More information about the GHSL data products can be found in the GHSL Data Package 2023 report ... |\n | [building](/earth-engine/datasets/tags/building) [built](/earth-engine/datasets/tags/built) [builtup](/earth-engine/datasets/tags/builtup) [copernicus](/earth-engine/datasets/tags/copernicus) [ghsl](/earth-engine/datasets/tags/ghsl) [height](/earth-engine/datasets/tags/height) |\n\n-\n\n |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### Tsinghua FROM-GLC Year of Change to Impervious Surface](/earth-engine/datasets/catalog/Tsinghua_FROM-GLC_GAIA_v10) |\n | This dataset contains annual change information of global impervious surface area from 1985 to 2018 at a 30m resolution. Change from pervious to impervious was determined using a combined approach of supervised classification and temporal consistency checking. Impervious pixels are defined as above 50% impervious. ... |\n | [built](/earth-engine/datasets/tags/built) [population](/earth-engine/datasets/tags/population) [tsinghua](/earth-engine/datasets/tags/tsinghua) [urban](/earth-engine/datasets/tags/urban) |"]]