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Google Earth Engine hosts a vast collection of geospatial datasets covering topics such as land cover, land use, climate, vegetation indices and more.
The `earthengine-api` Python library authenticates with Google Earth Engine and provides `ee.Image` and `ee.ImageCollection` objects. Engineers write server-side JavaScript-like Python code that executes in the cloud, reducing large computations to small JSON results.
Google Earth Engine datasets train AI remote sensing models for land cover classification, change detection, and environmental monitoring. Process Sentinel-2 or Landsat imagery at global scale to generate training labels for vision AI, or use computed indices (NDVI, NDWI) as features for crop yield AI models.
# pip install earthengine-api
import ee
ee.Authenticate()
ee.Initialize(project="YOUR_PROJECT_ID")
# Compute mean NDVI for Europe in 2023
ndvi = (ee.ImageCollection("MODIS/061/MOD13A3")
.filterDate("2023-01-01", "2023-12-31")
.select("NDVI")
.mean()
.multiply(0.0001))
print("NDVI image loaded:", ndvi.bandNames().getInfo())Official dataset source
More datasets used by Python data engineers.
NOAA platform provides access to a vast collection of climate-related datasets, including historical weather data, climate observations, satellite imagery and climate model outputs.
The NCEI, part of NOAA, provides access to a wide range of environmental datasets, including climate data, weather observations, oceanographic data and geophysical data.
EOSDIS provides access to a wide range of Earth observation datasets, including satellite imagery, climate data, land cover, oceanography and atmospheric data.