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
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!
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.
It might work. TESSERA's embeddings are at a 10 metre resolution, so it might depend on the size of the features you are looking for. If those features have distinct changes in colour or texture over time or they scatter radar in different ways compared with their surroundings then you should be able to discriminate them.
The easiest way to test is to try out the interactive notebook and drop some labels in known areas.
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?
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.
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.
Not much detail on the method? Like what data it takes from iNaturalist - for example if it's taking in GPS coordinates of observations of brambles then it's not clear what there is for the ML model to do.
What detail was in the satellite images, was it taking signals of the type of spaces brambles are in, or was it just visually identifying bramble patches?
In the UK you get brambles in pretty much every non-cultivated green space. I wonder how well the classifier did?
Hi! You can find a bit more about Gabriel's model through some of his posts over the last few weeks: https://gabrielmahler.org/posts/
When it comes to the satellite images, the model actually used TESSERA (https://arxiv.org/abs/2506.20380) which is a model we trained to produce embeddings for every point on earth that encodes the temporal-spectral properties over a year.
Think of it like a compression of potentially fifty or a hundred observations of a particular point in earth down to a single 128 dimension vector.
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.
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.
FarmLogs (YC 12) did exactly this. We used sat imagery in the near-infrared spectrum to determine crop health remotely. Modern farming utilizes a practice called precision ag - where your machine essentially has a map of zones on the field for where treatments are or aren't needed and controllers that can turn spray nozzles on/off depending on boundaries. We used sat imagery as the base for an automated prescription system, too. So a farmer can reduce waste by only applying fertilizer or herbicide in specific areas that need it.
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:
Because the embeddings are precomputed, the library just has to download the tiles from our server. More at: https://anil.recoil.org/notes/geotessera-pythonDownstream 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!
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.
It might work. TESSERA's embeddings are at a 10 metre resolution, so it might depend on the size of the features you are looking for. If those features have distinct changes in colour or texture over time or they scatter radar in different ways compared with their surroundings then you should be able to discriminate them.
The easiest way to test is to try out the interactive notebook and drop some labels in known areas.
almost definitely!
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?
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.
It is good to enable people to hedge against bad harvests.
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.
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
Not much detail on the method? Like what data it takes from iNaturalist - for example if it's taking in GPS coordinates of observations of brambles then it's not clear what there is for the ML model to do.
What detail was in the satellite images, was it taking signals of the type of spaces brambles are in, or was it just visually identifying bramble patches?
In the UK you get brambles in pretty much every non-cultivated green space. I wonder how well the classifier did?
Interesting project.
Hi! You can find a bit more about Gabriel's model through some of his posts over the last few weeks: https://gabrielmahler.org/posts/
When it comes to the satellite images, the model actually used TESSERA (https://arxiv.org/abs/2506.20380) which is a model we trained to produce embeddings for every point on earth that encodes the temporal-spectral properties over a year.
Think of it like a compression of potentially fifty or a hundred observations of a particular point in earth down to a single 128 dimension vector.
Happy to answer any other questions.
If it can find sloes it's going to make sloe gin foragers very very angry. Generally when they find a usable crop they don't share it.
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.
Related, I think? Satellite + AI = finding things, not sure if similar beyond that
https://www.pnas.org/doi/10.1073/pnas.2407652121
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.
FarmLogs (YC 12) did exactly this. We used sat imagery in the near-infrared spectrum to determine crop health remotely. Modern farming utilizes a practice called precision ag - where your machine essentially has a map of zones on the field for where treatments are or aren't needed and controllers that can turn spray nozzles on/off depending on boundaries. We used sat imagery as the base for an automated prescription system, too. So a farmer can reduce waste by only applying fertilizer or herbicide in specific areas that need it.