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GHSL: 2018년 전 세계 건물 높이 (P2023A)
이 공간 래스터 데이터 세트는 2018년을 기준으로 100m 해상도로 건물 높이의 전 세계 분포를 보여줍니다. 건물 높이를 예측하는 데 사용되는 입력 데이터는 ALOS 글로벌 디지털 표면 모델 (30m), NASA 셔틀 레이더 지형지물 매핑 … alos 건물 건축됨 건축 환경 건물 코페르니쿠스 -
GHSL: 전 세계 인공 표면 10m (P2023A)
이 래스터 데이터 세트는 S2 이미지 데이터에서 관찰된 2018년의 10m 그리드 셀당 평방미터로 표시된 인공 표면의 분포를 보여줍니다. 데이터 세트는 a) 총 건물 면적, b) 격자 셀에 할당된 건물 면적을 측정합니다. built built-environment builtup copernicus ghsl jrc -
GHSL: 전 세계 1975~2030년 인공 표면 (P2023A)
이 래스터 데이터 세트는 100m 그리드 셀당 평방미터로 표시된 인공 표면의 분포를 보여줍니다. 이 데이터 세트는 a) 총 건축물 면적과 b) 주로 비주거용 (NRES) 용도의 그리드 셀에 할당된 건축물 면적을 측정합니다. 데이터가 공간적으로 보간되거나 … built built-environment builtup copernicus ghsl jrc -
GHSL: 전 세계 결제 특성 (10m) 2018 (P2023A)
이 공간 래스터 데이터 세트는 10m 해상도로 인구 거주 지역을 표시하고, 인공 환경의 기능 및 높이 관련 구성요소 측면에서 내부 특성을 설명합니다. GHSL 데이터 제품에 대한 자세한 내용은 GHSL 데이터 패키지 2023 보고서에서 확인할 수 있습니다. 건물 건축됨 건물 코페르니쿠스 ghsl 높이 -
칭화 FROM-GLC 불투수 표면으로 변경 연도
이 데이터 세트에는 1985년부터 2018년까지 전 세계 불투수 표면적의 연간 변화 정보가 30m 해상도로 포함되어 있습니다. 투수성에서 불투수성으로의 변화는 감독 분류와 시간적 일관성 확인의 결합된 접근 방식을 사용하여 결정되었습니다. 불투명 픽셀은 불투명도가 50% 를 초과하는 것으로 정의됩니다. … 건축 인구 칭화 도시
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) |"]]