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Can a model trained on satellite data really find brambles on the ground?

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Re: Can a model trained on satellite data really find brambles on the ground?

#11

Post by Waterluvian »

CrazyStat wrote: post #7
Waterluvian wrote: post #5

Earlier quoted context omitted.

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 an…

> 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?

Every time someone explains the way short selling or speculative markets work, I have a “oh, I get it…” moment and then forget months later.

Same with insurance… socialized risk for our food supply is objectively good, and protecting the insurance mechanism from fraud is good. People can always bastardize these things.

Re: Can a model trained on satellite data really find brambles on the ground?

#12

Post by avsm »

cuno wrote: post #2

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?

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!

Re: Can a model trained on satellite data really find brambles on the ground?

#13

Post by sadiq »

Waterluvian wrote: post #5
cuno wrote: post #2

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?

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 an…

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

Re: Can a model trained on satellite data really find brambles on the ground?

#14

Post by avsm »

0_____0 wrote: post #9
cuno wrote: post #2

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?

I've wondered this about finding hot springs.

That's should be a pretty good usecase; if you do just a few labels manually of known hotsprings you should be able to find others quite quickly using the TESSERA interactive notebook. The embeddings capture the annual spectral-temporal signature, so a hotspring should be fairly distinctive vs the surroundings.

Video of the notebook in action https://crank.recoil.org/w/mDzPQ8vW7mkLjdmWsW8vpQ and the source https://github.com/ucam-eo/tessera-interactive-map

Re: Can a model trained on satellite data really find brambles on the ground?

#16

Post by wbl »

CrazyStat wrote: post #7
Waterluvian wrote: post #5

Earlier quoted context omitted.

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 an…

> 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?

It is good to enable people to hedge against bad harvests.

Re: Can a model trained on satellite data really find brambles on the ground?

#18

Post by sadiq »

jcims wrote: post #15

Seems like it could be pretty useful for archaeology as well.

That's actually a great idea! I wonder what kind of feature size would be needed though - TESSERA's embeddings are at a 10 metre resolution so for larger structures you might need some kind of spatial aggregation.

Re: Can a model trained on satellite data really find brambles on the ground?

#19

Post by sadiq »

siva7 wrote: post #17

can it find me truffles?

If you have some GPS locations of truffles, you could use the notebook Anil mentioned here https://news.ycombinator.com/item?id=45378855 and give it a go.

There is the issue of just how visible truffles are from space though, if they grow under cover. That said, it may still work because you can find habitats that are very likely to have truffles. We've had some promising results looking at fungal biomass.

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