https://gabrielmahler.org/environment/ai/ml/%F0%9F%A6%94/202...

For the "However, it is obvious that most of the generated findings aren’t brambles"

isn't this the same findings as the old "we trained to identify huskies, but instead we identified snow" problem?

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!

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.

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.

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.

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.

As a hobby project, I was looking into using LiDAR data to view archeological points of interest in Switzerland: https://github.com/r-follador/delta-relief

It would be interesting to overlay TESSERA data there, although the resolution is of course very different.

Related, I think? Satellite + AI = finding things, not sure if similar beyond that

https://www.pnas.org/doi/10.1073/pnas.2407652121

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.