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cuno | 5 months ago

So after transforming multispectral satellite data into a 128-dimensional embedding vector you can play "Where's Wally" to pinpoint blackberry bushes? I hope they tasted good! I'm guessing you can pretty much pinpoint any other kind of thing as well then?

discuss

order

avsm|5 months ago

Yes it's very good fun just exploring the embeddings! It's all wrapped by the geotessera Python library, so with uv and gdal installed just try this for your favourite region to get a false-colour map of the 128-dimensional embeddings:

  # for cambridge
  # https://github.com/ucam-eo/geotessera/blob/main/example/CB.geojson
  curl -OL https://raw.githubusercontent.com/ucam-eo/geotessera/refs/heads/main/example/CB.geojson
  # download the embeddings as geotiffs
  uvx geotessera download --region-file CB.geojson -o cb2
  # do a false colour PCA down to 3 dimensions from 128
  uvx geotessera visualize cb2 cb2.tif
  # project onto webmercator and visualise using leafletjs over openstreetmap
  uvx geotessera webmap cb2.tif --output cb2-map --serve
Because the embeddings are precomputed, the library just has to download the tiles from our server. More at: https://anil.recoil.org/notes/geotessera-python

Downstream classifiers are really fast to train (seconds for small regions). You can try out a notebook in VSCode to mess around with it graphically using https://github.com/ucam-eo/tessera-interactive-map

The berries were a bit sour, summer is sadly over here!

throwup238|5 months ago

This is all far outside of my wheel house but I'm curious if there's any way to use this for rocks and geology? Identifying dikes and veins on cliff sides from satellites would be really cool.

Waterluvian|5 months ago

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.

sadiq|5 months ago

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

CrazyStat|5 months ago

> 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?

sadiq|5 months ago

Yes! TESSERA is very new so we're still exploring how well it works for various things.

We're hoping to try it with a few different things for our next field trip, maybe some that are much harder to find than brambles.

0_____0|5 months ago

I've wondered this about finding hot springs.