I haven’t done this kind of thing since undergrad, but hyperspectral data is really frickin cool this way. Not only can you use spectral signatures to identify specific things, but also figure out what those things are made out of by unmixing the spectra.

For example, figure out what crop someone’s growing and decide how healthy it is. With sufficient temporal resolution, you can understand when things are planted and how well they’re growing, how weedy or infiltrated they are by pest plants, how long the soil remains wet or if rainwater runs off and leaves the crop dry earlier than desired. Etc.

If you’re a good guy, you’d leverage this data to empower farmers. If you’re an asshole, you’re looking to see who has planted your crop illegally, or who is breaking your insurance fine print, etc.

Hyperspectral data is really neat though it's worth pointing out that TESSERA is only trained on multispectral (optical + SAR) data.

You are very right on the temporal aspect though, that's what makes the representation so powerful. Crops grow and change colour or scatter patterns in distinct ways.

It's worth pointing out the model and training code is under an Apache2 license and the global embeddings are under a CC-BY-A. We have a python library that makes working with them pretty easy: https://github.com/ucam-eo/geotessera

> If you’re a good guy, you’d leverage this data to empower farmers. If you’re an asshole, you’re looking to see who has planted your crop illegally, or who is breaking your insurance fine print, etc.

How does using it to speculate on crop futures rank?