Challenge closed
Large Earth foundation models have the potential to revolutionize how we use satellite data to address environmental and social challenges. We launched the AI for Earth 2024 Challenge to encourage the development and application of large Earth models for impact. We invite engineers, scientists, researchers, and enthusiasts to join us!
Large Earth foundation models can transform interaction with Earth observation (EO) data by easily ingesting and processing large satellite datasets for use on diverse tasks. They efficiently process vast satellite datasets for diverse tasks, reducing costs and unlocking valuable Earth insights. While the field is growing, limited budgets hinder scaling up model training and testing. The hefty demands of model development and shared global challenges we face underscore the importance of working collaboratively to deploy these models to solve pressing social, environmental, and climate issues. Clay and the #AIForEarthChallenge2024 co-organizers designed this challenge to stimulate development and application of large Earth foundation models towards a wide range of impactful uses.
Open science approaches necessitate collective metrics to make progress towards. Although there are many proposed standards for benchmarks, we lack a common benchmark to measure real impact. This challenge is kickstarting our collective effort to build a common benchmark based on the Common Task Framework principles, enabling researchers and organizations to deploy and test models on environmental tasks. It represents a commitment toward collaborating in the open to scale solutions for climate, nature, and people (if you’re interested in being involved in defining benchmarks, Clay has launched a collaborative benchmarking working group, kicked off by this prize). Ultimately, concentrating efforts and publicly available results will drive innovation and provide open information about model performance, helping users select the best models for their needs. This challenge, co-organized by the Clark University Center for Geospatial Analytics, Clay, Development Seed, Ode, and the Taylor Geospatial Institute, aims to unlock new insights about the Earth, develop powerful tools to better understand and protect our planet, and create a benchmark to reward AI satellite models with operational excellence. It is supported through Clay's Social Impact Partnership with Amazon Web Services.
Foundation models hold promise to be effective across various downstream tasks to help solve pressing challenges for people and the planet, but they must be built with these applications in mind.
We have chosen seven tasks intended to highlight the operational value of foundation models and the type of signal and function needed, as proposed here.
Type of signal anchor: Spatial, Spectral, and Temporal Type of functions: Object Detection, Change Detection, Classification, Regression, Generative
Your predictions for each challenge will be tested on either a subset of the reference data or an analogous dataset over a different region.
Participants are required to develop one Apache-licensed Jupyter Notebook in Python that can perform the following tasks with the following constraints:
Please review this example pseudocode submission notebook to clarify what is expected in terms of submission content and format and this template submission notebook to access evaluation code that you must incorporate into your submission. If it’s helpful, this repository also provides basic code solutions that you can build off of for your submission.
AI4EO is carried out under a programme of, and funded by the European Space Agency (ESA).
Disclaimer: The views expressed on this site shall not be construed to reflect the official opinion of ESA.
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