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.
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.
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.
cuno•49m ago
Waterluvian•38m ago
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.
CrazyStat•33m ago
How does using it to speculate on crop futures rank?
Waterluvian•30m ago
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.
wbl•21m ago
sadiq•27m ago
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
sadiq•33m ago
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•32m ago
avsm•24m ago
Video of the notebook in action https://crank.recoil.org/w/mDzPQ8vW7mkLjdmWsW8vpQ and the source https://github.com/ucam-eo/tessera-interactive-map
avsm•28m ago
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!