import boto3
import configparser
import os
import urllib3

import folium
import geopandas as gpd
import rasterio
from rasterio.plot import show
import numpy as np
from matplotlib import pyplot

import tempfile

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 1
----> 1 import boto3
      2 import configparser
      3 import os

ModuleNotFoundError: No module named 'boto3'

urllib3.disable_warnings()

Connection with S3 Bucket

All GHSL datasets are available on S3 Bucket. Below configuration allows to list and download defined datasets from there. There 6 GHSL products on bucket:

• GHS_BUILT_C

• GHS_BUILT_H

• GHS_BUILT_S

• GHS_BUILT_V

• GHS_POP

• GHS_SMOD

Useful links

• https://human-settlement.emergency.copernicus.eu/download.php

def s3_connection(credentials: dict) -> boto3.session.Session:
    """Establishes a connection to an S3 bucket.

    Args:
        credentials (dict): A dictionary containing AWS S3 credentials with keys 
                            'host_base', 'access_key', and 'secret_key'.

    Returns:
        boto3.session.Session: A boto3 session client configured with the provided 
                               credentials for interacting with the S3 service.
    """
    s3 = boto3.client('s3',
                      endpoint_url=credentials['host_base'],
                      aws_access_key_id=credentials['access_key'],
                      aws_secret_access_key=credentials['secret_key'],
                      use_ssl=True,
                      verify=False)
    return s3

# Load s3 credentials
config = configparser.ConfigParser()
config.read('/home/eouser/.s3cfg')
credentials = dict(config['default'].items())

# Connection with S3 eodata
s3 = s3_connection(credentials)

Browsing S3 bucket content

Prefix parameter is used to defining which datasets user would like to find on S3 bucket

# Listing all GHS_BUILT_S data for year 2020 and spatial resolution 100m
response = s3.list_objects_v2(Bucket='ESTAT', Prefix='GHSL/GHS_BUILT_S/2020/100m')

if 'Contents' in response:
    print("Objects in bucket:")
    # Iterate over each object
    for obj in response['Contents']:
        print(obj['Key'])
else:
    print("No objects found in the bucket.")

Objects in bucket:
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R11_C23.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R12_C24.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R1_C19.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R1_C20.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R2_C17.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R2_C18.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R2_C19.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R2_C20.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R2_C21.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R3_C18.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R3_C19.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R3_C20.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R3_C21.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R4_C18.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R4_C19.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R4_C20.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R4_C21.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R4_C22.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C16.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C17.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C18.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C19.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C20.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C21.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C22.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C23.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R6_C17.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R7_C12.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R7_C13.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R8_C12.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R8_C13.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R9_C13.tif

# Listing all GHS_BUILT_S data for year 2020 and all spatial resolutions
response = s3.list_objects_v2(Bucket='ESTAT', Prefix='GHSL/GHS_BUILT_S/2020')

if 'Contents' in response:
    print("Objects in bucket:")
    # Iterate over each object
    for obj in response['Contents']:
        print(obj['Key'])
else:
    print("No objects found in the bucket.")

Objects in bucket:
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R11_C23.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R12_C24.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R1_C19.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R1_C20.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R2_C17.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R2_C18.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R2_C19.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R2_C20.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R2_C21.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R3_C18.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R3_C19.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R3_C20.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R3_C21.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R4_C18.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R4_C19.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R4_C20.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R4_C21.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R4_C22.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R5_C16.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R5_C17.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R5_C18.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R5_C19.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R5_C20.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R5_C21.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R5_C22.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R5_C23.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R6_C17.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R7_C12.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R7_C13.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R8_C12.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R8_C13.tif
GHSL/GHS_BUILT_S/2020/1000m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_1000_V1_0_R9_C13.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R11_C23.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R12_C24.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R1_C19.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R1_C20.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R2_C17.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R2_C18.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R2_C19.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R2_C20.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R2_C21.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R3_C18.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R3_C19.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R3_C20.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R3_C21.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R4_C18.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R4_C19.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R4_C20.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R4_C21.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R4_C22.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C16.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C17.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C18.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C19.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C20.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C21.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C22.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R5_C23.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R6_C17.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R7_C12.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R7_C13.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R8_C12.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R8_C13.tif
GHSL/GHS_BUILT_S/2020/100m/GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0_R9_C13.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R11_C23.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R11_C24.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R12_C24.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R2_C18.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R2_C20.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R2_C21.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R2_C22.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R3_C16.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R3_C17.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R3_C18.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R3_C19.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R3_C20.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R3_C21.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R3_C22.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R4_C17.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R4_C18.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R4_C19.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R4_C20.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R4_C21.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R4_C22.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R4_C23.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R5_C15.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R5_C18.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R5_C19.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R5_C20.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R5_C21.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R5_C22.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R5_C23.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R6_C16.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R6_C17.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R6_C18.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R6_C19.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R6_C20.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R6_C21.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R6_C22.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R6_C23.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R7_C12.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R7_C17.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R8_C12.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R8_C13.tif
GHSL/GHS_BUILT_S/2020/30ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_30ss_V1_0_R9_C13.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R11_C23.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R11_C24.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R12_C24.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R2_C18.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R2_C20.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R2_C21.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R2_C22.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R3_C16.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R3_C17.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R3_C18.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R3_C19.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R3_C20.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R3_C21.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R3_C22.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R4_C17.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R4_C18.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R4_C19.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R4_C20.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R4_C21.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R4_C22.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R4_C23.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R5_C15.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R5_C18.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R5_C19.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R5_C20.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R5_C21.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R5_C22.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R5_C23.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R6_C16.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R6_C17.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R6_C18.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R6_C19.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R6_C20.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R6_C21.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R6_C22.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R6_C23.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R7_C12.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R7_C17.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R8_C12.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R8_C13.tif
GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R9_C13.tif

Reading vector file to GeoDataFrame

object_path = 'GHSL/TILES/GHSL_tiles_Europe_4326.gpkg'

# Create a temporary directory to store GeoPackage file
with tempfile.TemporaryDirectory() as tmpdirname:
    # Define local path to save GeoPackage file
    local_geopackage_path = os.path.join(tmpdirname, object_path.split('/')[-1])

    # Download the GeoPackage from S3
    s3.download_file('ESTAT', object_path, local_geopackage_path)

    # Read the GeoPackage into a GeoDataFrame
    gdf = gpd.read_file(local_geopackage_path)

# Geodata parameters
print(gdf.info())
print('----')
print(f'Coordinate system: {gdf.crs}')

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 41 entries, 0 to 40
Data columns (total 6 columns):
 #   Column    Non-Null Count  Dtype   
---  ------    --------------  -----   
 0   tile_id   41 non-null     object  
 1   left      41 non-null     float64 
 2   top       41 non-null     float64 
 3   right     41 non-null     float64 
 4   bottom    41 non-null     float64 
 5   geometry  41 non-null     geometry
dtypes: float64(4), geometry(1), object(1)
memory usage: 2.0+ KB
None
----
Coordinate system: EPSG:4326

gdf.head()

	tile_id	left	top	right	bottom	geometry
0	R7_C12	-70.007916	29.099583	-60.007916	19.099583	POLYGON ((-60.00792 29.09958, -60.00792 19.099...
1	R8_C12	-70.007916	19.099583	-60.007916	9.099583	POLYGON ((-60.00792 19.09958, -60.00792 9.0995...
2	R8_C13	-60.007916	19.099583	-50.007916	9.099583	POLYGON ((-50.00792 19.09958, -50.00792 9.0995...
3	R9_C13	-60.007916	9.099583	-50.007916	-0.900416	POLYGON ((-50.00792 9.09958, -50.00792 -0.9004...
4	R5_C15	-40.007916	49.099583	-30.007917	39.099583	POLYGON ((-30.00792 49.09958, -30.00792 39.099...

GeoDataFrame explanation

GeoDataFrame inherits most of pandas DataFrame methods. That allows to work with GeoDataFrame on the same way.

# Creating new GeoDataFrame with defined columns
gdf[gdf.tile_id == 'R7_C12']

	tile_id	left	top	right	bottom	geometry
0	R7_C12	-70.007916	29.099583	-60.007916	19.099583	POLYGON ((-60.00792 29.09958, -60.00792 19.099...

Displaying geometries on basemap

To display vector geometry on map we recommend folium. Folium allows displaying different types of geometries like Polygons, Lines and Points.
IMPORTANT: Each geometry presenting on map must be transformed to EPSG:4326 coordinates system

# Add the polygons to the map

m1 = folium.Map(location=[30.0, 0.0], zoom_start=2)

for _, r in gdf.iterrows():
    sim_geo = gpd.GeoSeries(r["geometry"])
    geo_j = sim_geo.to_json()
    geo_j = folium.GeoJson(data=geo_j, style_function=lambda x: {"fillColor": "orange"})
    folium.Popup(r["tile_id"]).add_to(geo_j)
    geo_j.add_to(m1)

m1

Make this Notebook Trusted to load map: File -> Trust Notebook

Reading raster file

Geographic information systems use GeoTIFF and other formats to organize and store gridded, or raster, datasets. Rasterio reads and writes these formats and provides a Python API based on N-D arrays.
More information: https://rasterio.readthedocs.io/en/stable/quickstart.html

# S3 reference to GeoTIFF
object_path = 'GHSL/GHS_BUILT_S/2020/3ss/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0_R5_C19.tif'

# Create a temporary directory to store the raster file
with tempfile.TemporaryDirectory() as tmpdirname:
    # Define local path to save raster file
    local_raster_path = os.path.join(tmpdirname, object_path.split('/')[-1])

    # Download raster from S3
    s3.download_file('ESTAT', object_path, local_raster_path)

    # Read raster into numpy array
    with rasterio.open(local_raster_path) as src:
        raster_data = src.read()
        raster_metadata = src.meta

# Printing metadata

print(src.meta) # Raster total metadata
print('---')
print(src.width, src.height) # Numner of rows and columns in raster
print(src.crs) # Coordinate system
print(src.transform) # Parameters of affine transformation
print(src.count) # Number of bands

{'driver': 'GTiff', 'dtype': 'uint16', 'nodata': None, 'width': 12000, 'height': 12000, 'count': 1, 'crs': CRS.from_wkt('GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]'), 'transform': Affine(0.0008333333300326821, 0.0, -0.00791664424099281,
       0.0, -0.0008333333299795073, 49.09958333862926)}
---
12000 12000
EPSG:4326
| 0.00, 0.00,-0.01|
| 0.00,-0.00, 49.10|
| 0.00, 0.00, 1.00|
1

# Raster data is read as numpy array
print(type(raster_data))
print(raster_data.shape)
raster_data

<class 'numpy.ndarray'>
(1, 12000, 12000)

array([[[0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        ...,
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint16)

Plotting raster file

There are couple of options to plot raster file. Below we present most popular option using matplotlib.pyplot module and tool implemented in rasterio package. More information:

• https://matplotlib.org/3.5.3/api/_as_gen/matplotlib.pyplot.html

• https://rasterio.readthedocs.io/en/stable/topics/plotting.html

# Plotting definition
fig, ax = pyplot.subplots(1, figsize=(20,10))

# Plotting raster
pyplot.imshow(raster_data[0], cmap='pink')

# Plotting color bar
pyplot.colorbar(label='Parameter name', fraction=0.02, ax=ax)

<matplotlib.colorbar.Colorbar at 0x7f1ace89b970>

# Rasterio also provides rasterio.plot.show() to perform common tasks such as displaying 
# multi-band images as RGB and labeling the axes with proper geo-referenced extents.

# Plotting definition
fig, ax = pyplot.subplots(1, figsize=(20,10))

# Plotting raster with georeference
rs = show(raster_data[0], ax=ax, transform=src.transform)

# Plotting color bar
fig.colorbar(rs.get_images()[0], ax=ax, label='Parameter name')

<matplotlib.colorbar.Colorbar at 0x7f1acddb34f0>