Google Earth Engine Data Pipeline

Automating Environmental Data Collection for Faster, Reliable Analysis

Environmental analysis typically begins with tedious data preparation: identifying relevant satellite imagery, downloading datasets, aligning projections and resolutions, clipping to study areas, and combining everything for analysis. This manual work consumes days before analysis begins and must be repeated for every new project, often requiring GIS expertise that conservation teams lack.

I developed a Python module that automates global environmental data collection through the Google Earth Engine API. A user provides a polygon defining their study area, and the module handles selection of appropriate satellite imagery and environmental datasets, applies necessary processing and quality filters, combines multiple data sources, and exports results either as raster files or as gridded point data with values extracted from cell centroids.

The module integrates with the Global Biodiversity Information Facility to pull species occurrence records, and can also incorporate locally stored datasets. Additional utility functions handle common preprocessing tasks.

This module now serves as core infrastructure for both the habitat suitability modeling platform and WildMon’s broader data pipeline. What previously required days of specialist work now takes hours and can be performed by anyone with basic Python knowledge, while ensuring every project uses the same rigorous data collection workflow.