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GHSL: Global building height 2018 (P2023A)
Dieser räumliche Rasterdatensatz zeigt die globale Verteilung der Gebäudehöhen mit einer Auflösung von 100 m für das Jahr 2018. Die Eingabedaten zur Vorhersage der Gebäudehöhen sind das ALOS Global Digital Surface Model (30 m), die NASA Shuttle Radar Topographic Mission … alos building built built-environment builtup copernicus -
GHSL: Globale bebaute Fläche 10 m (P2023A)
Dieser Rasterdatensatz zeigt die Verteilung bebauter Flächen in Quadratmetern pro 10-m-Rasterzelle für 2018, wie sie aus den S2-Bilddaten hervorgeht. Die Datensätze messen: a) die bebaute Fläche insgesamt und b) die bebaute Fläche, die Rasterzellen mit einer … built built-environment builtup copernicus ghsl jrc -
GHSL: Global bebaute Fläche 1975–2030 (P2023A)
Dieser Rasterdatensatz stellt die Verteilung bebauter Flächen in Quadratmetern pro 100-m-Rasterzelle dar. Der Datensatz misst: a) die bebaute Fläche insgesamt und b) die bebaute Fläche, die Rasterzellen mit überwiegend nicht-wohnlicher Nutzung (NRES) zugewiesen ist. Die Daten werden räumlich und zeitlich interpoliert oder… built built-environment builtup copernicus ghsl jrc -
GHSL: Global settlement characteristics (10 m) 2018 (P2023A)
Dieser räumliche Rasterdatensatz zeigt menschliche Siedlungen mit einer Auflösung von 10 m und beschreibt ihre inneren Merkmale in Bezug auf die funktionalen und höhenbezogenen Komponenten der bebauten Umwelt. Weitere Informationen zu den GHSL-Datenprodukten finden Sie im Bericht „GHSL Data Package 2023“ (GHSL-Datenpaket 2023)… building built builtup copernicus ghsl height -
Tsinghua FROM-GLC Jahr der Änderung zu einer undurchlässigen Oberfläche
Dieser Datensatz enthält Informationen zu den jährlichen Veränderungen der globalen bebauten Fläche von 1985 bis 2018 mit einer Auflösung von 30 m. Die Änderung von durchlässig zu undurchlässig wurde mit einem kombinierten Ansatz aus einer beaufsichtigten Klassifizierung und einer Prüfung der zeitlichen Konsistenz ermittelt. Nicht befahrbare Pixel sind definiert als mehr als 50% nicht befahrbar. … gebaut Bevölkerung Tsinghua urban
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) |"]]