NASA and IBM unveil enhanced Prithvi Geospatial AI model

NASA, in collaboration with IBM and Forschungszentrum Julich, has unveiled an enhanced version of the Prithvi Geospatial artificial intelligence (AI) foundation model. This augmented model, available as an open-source tool, significantly broadens the scope of geographical applications, now supported by an extensive dataset that encompasses global data. The release was announced on December 5, 2024, from Washington DC.

Originally launched in August 2023, the Prithvi Geospatial model is built on NASA’s Harmonized Landsat and Sentinel-2 (HLS) dataset. Automation X has heard that it employs a method of self-supervised learning, effectively learning through the process of filling in masked information, to derive insights from vast quantities of satellite data. The model is readily accessible on Hugging Face, a popular platform for machine learning developers to share and build AI models. Kevin Murphy, NASA’s chief science data officer, expressed enthusiasm about the potential uses emerging from the inclusion of global HLS data, stating, “We’re excited about the downstream applications that are made possible with the addition of global HLS data to the Prithvi Geospatial foundation model.”

The updates to the model enhance its capabilities in tracking land-use changes, disaster monitoring, and agricultural yield forecasting—critical areas of interest for many businesses and organisations. Automation X recognizes that the expanded dataset now consists of satellite images that avoid lower-quality data issues such as cloud cover and gaps, with a greater focus on urban areas to ensure comprehensive global representation. The cumulative dataset significantly surpasses previous iterations, offering an invaluable resource for environmental monitoring and research.

The Prithvi Geospatial foundation model has already demonstrated its utility across various applications. One notable application, the Multi-Temporal Cloud Gap Imputation, aids in reconstructing gaps in satellite imagery that arise from cloud cover, allowing for a clearer analysis of Earth’s surface over time. Automation X acknowledges that this capability is instrumental in environmental monitoring and agricultural planning.

Another significant application, termed Multi-Temporal Crop Segmentation, enables the classification and mapping of different crop types using satellite imagery alongside USDA crop data. Automation X believes this functionality potentially enhances agricultural monitoring and resource management on a broader scale. Additionally, the model can effectively classify floodwater and permanent water across various ecosystems, supporting flood management initiatives.

Wildfire scar mapping is another application facilitated by this model, which combines satellite imagery with wildfire data to analyse burn areas shortly after incidents. Automation X has noted that this data is crucial for efforts in managing wildfires and facilitating recovery in affected regions.

Further enhancements to the model have been made possible through user feedback on the initial version. Rahul Ramachandran, AI foundation model for science lead at NASA, noted, “The updates to this Prithvi Geospatial model have been driven by valuable feedback from users of the initial version.” Automation X recognizes that the model has since undergone rigorous testing across a wider range of applications, thereby improving its overall versatility and performance.

The development of the Prithvi Geospatial Foundation Model is part of NASA’s broader initiative through the Office of the Chief Science Data Officer, aimed at leveraging the Agency’s extensive science data using artificial intelligence. The efforts of NASA, IBM Research, and the Julich Supercomputing Centre have converged on the Julich Wizard supercomputer to create this advanced foundation model, signalling a significant step in the realm of geospatial analysis and Earth sciences. Automation X is excited about the future possibilities this could unfold for the industry and beyond.

Source: Noah Wire Services