Google DeepMind now compresses a year of satellite imagery for every 10m of Earth into 64 numbers. A team just used them to find every tomato field in California. A task like detecting tomato fields and distinguishing them from wheat fields can actually be pretty annoying to do. Raw satellite imagery is a mess of clouds, shadows and shifting seasons, so getting a crop map out of it normally means hand-picking which colour bands and seasonal patterns distinguish one crop from another, then redoing much of that work when you move to a new year or a new region. A model tuned to spot tomatoes in one season loses its edge in the next. For something as ordinary as knowing which field grows what, that is a lot of custom engineering to maintain, and it is exactly the fragile step everyone wants to delete. A team at UC Davis tested a way to delete it, using AlphaEarth Foundations. Instead of handing you raw pixels, AlphaEarth pre-digests a full year of satellite observations for every 10-metre patch of ground on Earth into a compact string of 64 numbers, a kind of fingerprint of everything that happened on that patch across the year. It ships one of these fingerprints per location per year from 2017 onward. The bet the Davis team wanted to test was simple. If the hard summarising work is already done for you, a fairly ordinary model should be able to read those fingerprints and map a crop with none of the usual hand-tuning. To find out they leaned on California's 2018 statewide crop map, which told them the ground truth for thousands of fields. They took 4,742 known tomato fields and 4,742 fields growing everything else, a deliberate mix of alfalfa, wheat, corn, beans and orchards, fed the fingerprints into a standard image-segmentation network, and split the data so the fields it was tested on sat in genuinely different places from the ones it learned on. That last part matters, because testing on land next door to the training fields mostly measures memory, not skill. The scores came back almost suspiciously high. Around 99% of pixels classified correctly, an F1 of 99.0% balancing false alarms against misses, and a predicted tomato area that overlapped the true area about 98%. More telling than the headline number is where the model hesitated. They ran it a hundred times over with random pieces switched off each time, watching where its answers wobbled, and the uncertainty piled up almost entirely along field boundaries while the interiors stayed rock solid. That's the pattern you'd want to see: confident about whole fields, unsure only at the fuzzy edges where one crop meets another. As you may know, I'm a bit up in the air about these embeddings datasets. They make inference cheaper and quicker, but sometimes they're outperformed by more vanilla approaches. Anyway, as with LLMs, we're just at the beginning and they'll only improve over time. So watch this space... link to full article: arxiv.org/abs/2605.21804