Chapter 1. About geospatial inference

Geospatial models use the Vision Transformer (ViT) architecture to analyze satellite imagery and remote sensing data for applications such as environmental monitoring, land use classification, and climate analysis. Prithvi models are developed in collaboration with IBM and NASA.

IBM and NASA Prithvi geospatial foundation models are pre-trained on large datasets of satellite and aerial imagery. These models are trained on general representation of Earth observation data that can be fine-tuned for specific tasks.

Prithvi geospatial foundation models use a Vision Transformer (ViT) architecture that adapts the transformer model, originally designed for natural language processing, to process image data. ViT divides images into fixed-size patches, which are then processed as sequences similar to tokens in text.

For geospatial applications, ViT models can process multi-spectral satellite imagery with multiple input bands, enabling analysis beyond standard RGB imagery.

You can fine-tune geospatial foundation models using TerraTorch, an open-source library for fine-tuning and inference of geospatial foundation models.

You can find out more about the Prithvi models at huggingface.co/ibm-nasa-geospatial.