Global population grid data (1975-2030, interval 5 years) GHS Settlement Layers

Introduction to GHS Settlement Layers and Notebook Example

GHS Settlement Layers is the application of the urbanization degree (stage I) method recommended by the UN Statistical Commission to the global population grid data (1975-2030, 5-year interval) formed by JRC. Generated by integrating built-up areas (GHS-BUILT-S R2022) extracted from Landsat and Sentinel-2 imagery data and population data (GHS-POP R2022) derived from CIESIN GPW v4.11. This product has been updated based on the data released in 2019 according to the update of the GHS-BUILT-S and GHS-POP datasets. For specific information, please refer to the Preface – Artificial Intelligence Tutorial Global Human Settlement - GHS-SMOD_GLOBE_R2022A - European Commission

This data set is an international open data, which can be used for free for any commercial and non-commercial purposes.

See  Global Human Settlement - GHS-SMOD_GLOBE_R2022A - European Commission for more details on proper citation of this data product .

引用参考： Schiavina M., Melchiorri M., Pesaresi M. (2022): GHS-SMOD R2022A - GHS settlement layers, application of the Degree of Urbanisation methodology (stage I) to GHS-POP R2022A and GHS-BUILT-S R2022A, multitemporal (1975-2030)European Commission, Joint Research Centre (JRC) PID: Joint Research Centre Data Catalogue - GHS-SMOD R2022A - GHS settlement layers, applicati... - European Commission, doi: 10.2905/4606D58A-DC08-463C-86A9-D49EF461C47F European Commission, and Statistical Office of the European Union, 2021 Applying the Degree of Urbanisation — A methodological manual to define cities, towns and rural areas for international comparisons — 2021 edition Publications Office of the European Union, 2021; ISBN 978-92-76-20306-3 10.2785/706535

resolution

1000

band

name	band description	Minimum (estimated)	maximum value (estimated value)
smod_code	Settlement classification of the cell	-200	30

smod_code Class Table

Value	describe	color
10	Water grid cell	0C1D60
11	Very low density rural grid cell	CCF57A
12	Low density rural grid cell	AACE65
13	Rural cluster	5E8040
21	Suburban or peri-urban	FBFF55
22	Semi-dense urban cluster	B68F2F
23	Dense urban cluster	B68F2F
30	Urban center	EB5E58
-200	/	no data

code:

import aie
aie.Authenticate()
aie.Initialize()

# 指定需要检索的区域
feature_collection = aie.FeatureCollection('China_Province') \
                        .filter(aie.Filter.eq('province', '浙江省'))

geometry = feature_collection.geometry()


dataset = aie.ImageCollection('GHS_SMOD_GLOBE_R2022A') \
             .filterBounds(geometry) \
             .limit(10);

map = aie.Map(
    center=dataset.getCenter(),
    height=800,
    zoom=4
)

vis_params = {
    'bands': ['smod_code'],
    'min': 10,
    'max': 30,
    "palette":["#0C1D60","#CCF57A","#AACE65",
               "#5E8040","#FBFF55","#B68F2F",
               "#B68F2F","#EB5E58"]
}

map.addLayer(
    dataset,
    vis_params,
    'GHS_SMOD_GLOBE_R2022A',
    bounds=dataset.getBounds()
)
map

 

 Single scene image loading:

import aie
aie.Authenticate()
aie.Initialize()

img = aie.Image('GHS_SMOD_E1975_GLOBE_R2022A_54009_1000_V1_0_R10_C1')

map = aie.Map(
    center=img.getCenter(),
    height=800,
    zoom=4
)

vis_params = {
    'bands': ['smod_code'],
    'min': 10,
    'max': 30,
    "palette":["#0C1D60","#CCF57A","#AACE65",
               "#5E8040","#FBFF55","#B68F2F",
               "#B68F2F","#EB5E58"]
}

map.addLayer(
    img,
    vis_params,
    'GHS_SMOD_GLOBE_R2022A',
    bounds=img.getBounds()
)
map