Skip to content

Building Data Commons

I am a firm believer that Communities are what communities build together.The power of Google Earth Engine (GEE) lies not just in its processing capabilities, but also in its vibrant community. This community thrives on constant innovation and collaboration, evident in the ongoing iterations and shared code libraries. Inspired by this collaborative spirit, we embarked on a project to create a community-curated data repository – a space where users could contribute and access valuable geospatial datasets.

The impetus for this project arose from a specific user query. Someone inquired about Facebook's high-resolution population density maps, a dataset absent from the official GEE catalog. This presented a perfect opportunity to experiment with a community-driven data commons. The dataset, hosted by Columbia University, offered detailed population data at an impressive 30-meter resolution.You can read the foundational blog here

This Facebook dataset became the first and most frequently updated entry in the community catalog, now known as the #Awesome GEE Community Catalog.

Guiding Principle

The guiding principle behind this catalog draws inspiration from Elinor Ostrom's groundbreaking work on commons governance, a philosophy that has underpinned successful open-source projects like Linux and collaborative platforms like Wikipedia. Just as shared norms within a physical commons benefit everyone, fostering a similar collaborative environment within the digital realm was our goal.The idea was to use the inspiration from Digital Commons and create a Community Data Commons in the form of the #Awesome GEE Community Catalog.

83186_Awesome GEE Community Datasets_Flat_RD_New_092

The #Awesome GEE Community Catalog aims to reduce barriers for users by providing easy access to a growing collection of public datasets. This democratizes access to valuable geospatial data, similar to how GEE itself has democratized access to processing capabilities. However, the challenge lies in effectively applying these principles to both large-scale datasets and smaller, user-contributed ones. The Earth Engine ecosystem itself thrives on a culture of community learning, adaptation, and iteration.This community data commons serves as a bridge, connecting users with the datasets they need and fostering further collaboration within the GEE community. The #Awesome GEE Community Catalog represents a collaborative effort, and its continued success relies on the active participation of its users.