KoBold is accelerating the clean energy future by finding new battery metal deposits. This requires effectively leveraging various types of geoscience data and applying ML and physical computational techniques to guide exploration decisions.

We will walk through challenges we faced and lessons learned from developing and scaling scientific computation and ML techniques (FFT, derivative raster products, tree based ML models) to rasters, which requires a different set of considerations from scaling computation on tabular data. We will discuss key differences between computation of geospatial rasters vs. tabular data, and generalize our learnings to other climate change problem domains.