IBM and NASA teamed up to build the GPT of Earth sciences
The open-source model will serve as the basis for future forest, crop and climate change-monitoring AI.
NASA estimates that its Earth science missions will generate around a quarter million terabytes of data in 2024 alone. In order for climate scientists and the research community efficiently dig through these reams of raw satellite data, IBM, HuggingFace and NASA have collaborated to build an open-source geospatial foundation model that will serve as the basis for a new class of climate and Earth science AIs that can track deforestation, predict crop yields and rack greenhouse gas emissions.
For this project, IBM leveraged its recently-released Watsonx.ai to serve as the foundational model using a year’s worth of NASA’s Harmonized Landsat Sentinel-2 satellite data (HLS). That data is collected by the ESA’s pair of Sentinel-2 satellites, which are built to acquire high resolution optical imagery over land and coastal regions in 13 spectral bands.
For it’s part, HuggingFace is hosting the model on its open-source AI platform. According to IBM, by fine-tuning the model on “labeled data for flood and burn scar mapping,” the team was able to improve the model's performance 15 percent over the current state of the art using half as much data.
"The essential role of open-source technologies to accelerate critical areas of discovery such as climate change has never been clearer,” Sriram Raghavan, VP of IBM Research AI, said in a press release. “By combining IBM’s foundation model efforts aimed at creating flexible, reusable AI systems with NASA’s repository of Earth-satellite data, and making it available on the leading open-source AI platform, Hugging Face, we can leverage the power of collaboration to implement faster and more impactful solutions that will improve our planet.”
