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Global Forest Watch provides satellite-derived datasets on forest cover change, deforestation alerts, tree canopy loss, forest fires, and land use across all tropical countries. Used in data engineering for environmental monitoring pipelines, deforestation tracking systems, and geospatial analytics in Python.
Engineers use the Global Forest Watch API and downloadable GeoTIFF files. `rasterio` and `geopandas` process the raster forest cover data, while the GFW API provides vector alerts in GeoJSON format. `boto3` accesses GFW data stored in AWS S3 for large-scale analysis.
GFW satellite data trains AI models for deforestation detection and forest monitoring using computer vision. Convolutional neural networks classify satellite image patches as forest loss or gain. RAG systems built on GFW country profiles help AI climate tools answer questions about specific deforestation hotspots.
# pip install requests pandas geopandas
import requests, pandas as pd
# GFW tree cover loss by country (API)
resp = requests.get(
"https://data-api.globalforestwatch.org/dataset/umd_tree_cover_loss/latest/query",
params={"sql": "SELECT iso, umd_tree_cover_loss__ha FROM data WHERE umd_tree_cover_loss__year=2022 ORDER BY umd_tree_cover_loss__ha DESC LIMIT 10"}
)
df = pd.DataFrame(resp.json()["data"])
print(df)Official dataset source
More datasets used by Python data engineers.
Eurostat, the statistical office of the European Union, offers a comprehensive database of statistical data covering various domains such as economy, population, employment, environment and social issues.
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.