[ensocode]

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?

[sadiq]

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 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.

[ensocode]

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 back to the map. Does that sound right?

[sadiq]

So the interactive map should do this workflow for you. You place points and it will run the knn classifier over the landscape for you.

If you want to go further you can export the GeoJSON and then run it through any machine learning pipeline you like.

[avsm]

...and if you do build this @ensocode, feel free to open a PR to https://github.com/ucam-eo/geotessera and I'll incorporate it as an example in the repo.