41–50 of 55 posts
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.
Brambles are blackberries. Sloes are from Blackthorn bushes. They are different plants but probably are in the same location!
Well looks like they found a lot of brambles! Were there large areas without any bramble? Cue dowsers, who successfully find water... but also who would anyway anywhere else because underground water isn't the underground river/pocket that people imagine and thus random chance by itself has high probability of finding water.
For a proper evaluation you would need to be more methodological but as a sanity-check we were very happy with it.
One other thing to point out about the bramble model is that it is pixel-wise. That is each prediction is exclusively only what is within the 10 metre pixel (give or take the georeferencing error).
> Can a model trained on satellite data really find brambles on the ground? No, as per researcher, "However, it is obvious that most of the generated findings aren’t brambles" and obviously no. All the model did was think they followed roads, all roads. If it was oil and gas where people put in effort and their results where checked vs universities where meaningless citations matter and results are never confirmed, i…
As I mentioned in one of the other comments, the model is also only pixel-wise. That is, it is not using spatial information for predictions.
Earlier quoted context omitted.
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 are…
Is there a way to cluster the embeddings spatially or look for patterns isolated so some dimensions? (Again, way out of my wheel house) What I mean is a vein is usually a few meters wide but can be hundreds of meters long so ten meter resolution is probably not very helpful unless the embeddings can encode some sort of pattern that stretches across many cells.
The downside of that approach is that you need to spend valuable labels on learning the spatial feature extraction during training. To fix that we're working on building some pre-trained spatial feature extractors that you should only need to minimally fine-tune.
We have a problem with Giant Hogweed and I was thinking about ways to identify hotspots. My guess is that standard satellite imagery, like Google Maps, probably isn’t good enough. To even check if this could work, you’d need high-res imagery (sub-meter), ideally multispectral, and some way to validate it on the ground. What steps should I take to verify if this is possible in a way this was done here?
https://github.com/ucam-eo/geotessera has an image showing our embedding coverage at the moment. Blue areas we have complete coverage for 2024, green areas we cover 2017-2024. We're slowly trying to populate everything 2017-2024 but the constraint is GPU and storage at the moment - each year takes ~20k GPU/200k CPU hours and requires storing and serving 200 terabytes of data. The world is big!
If there is an area you would like prioritised, there's an issue template on the geotessera github repo which we can use to move regions around in the processing queue.
We have a problem with Giant Hogweed and I was thinking about ways to identify hotspots. My guess is that standard satellite imagery, like Google Maps, probably isn’t good enough. To even check if this could work, you’d need high-res imagery (sub-meter), ideally multispectral, and some way to validate it on the ground. What steps should I take to verify if this is possible in a way this was done here?
I would try https://github.com/ucam-eo/tessera-interactive-map , this is relatively easy to get started with and has a nice interface for labeling. https://github.com/ucam-eo/geotessera has an image showing our embedding coverage at the moment. Blue areas we have complete coverage for 2024, green areas we cover 2017-2024. We're slowly trying to populate everything 2017-2024 but the constraint is GPU and storage at th…
Earlier quoted context omitted.
I would try https://github.com/ucam-eo/tessera-interactive-map , this is relatively easy to get started with and has a nice interface for labeling. https://github.com/ucam-eo/geotessera has an image showing our embedding coverage at the moment. Blue areas we have complete coverage for 2024, green areas we cover 2017-2024. We're slowly trying to populate everything 2017-2024 but the constraint is GPU and storage at th…
Thanks for your explanation. For my region, 2024 coverage is already available, which should be sufficient to get started. After looking into the library, I just want to make sure I understand the workflow correctly: I would use the Tessera interactive map to mark known locations of Giant Hogweed, label them, and export as GeoJSON; then train a k-NN model, make predictions, and finally export the results as a GeoJSON…
If you want to go further you can export the GeoJSON and then run it through any machine learning pipeline you like.
Earlier quoted context omitted.
> Hyperspectral in the SWIR range is what you really want for this, but that's a whole different ball game. Are there any hyperspectral surveys with UAVs etc instead of satellites?
Usually airplanes because the instruments are heavy. But yeah, that's the most common case. Hyperspectral sats are much rarer than aerial hyperspectral.