How Does OlmoEarth Enable Geospatial Inference?

The OlmoEarth Platform is a game-changer in the field of geospatial inference, enabling organizations to run massive inference jobs across vast areas - think entire continents. This is especially crucial for environmental applications like deforestation monitoring, food security, and wildfire risk assessment. The platform is built on the OlmoEarth models, a family of Earth observation foundation models that have been pretrained on a whopping 10 terabytes of multimodal satellite data. These models can be fine-tuned and evaluated for specific use cases, and the OlmoEarth Platform provides the necessary infrastructure to scale them up.

One of the biggest hurdles in satellite inference is the sheer volume of data involved - we're talking terabytes. A single job fine-tuning a foundation model can take hours to complete, and the inputs can be all over the map (literally), spanning multiple spectral bands, sensor types, and time steps across a huge geographic area. To tackle this, the OlmoEarth Platform breaks each job down into three stages: data acquisition and preprocessing, inference, and postprocessing. Each stage is matched with a specific hardware profile - CPUs handle the prep work, GPUs take care of the inference, and CPUs wrap things up with postprocessing.

The platform then distributes these stages across multiple machines, keeping those GPUs humming and minimizing downtime spent on data prep. This means the platform can churn through large areas quickly and efficiently, making it possible to run inference jobs across entire continents in about a day. The results are then stitched together into seamless, geographically consistent maps, giving organizations the insights they need to make a real impact on environmental issues. As the platform continues to evolve, it's likely to make some serious waves in the field of geospatial inference, helping organizations make more informed decisions and take more effective action to protect the planet.

Source: Hugging Face