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Can a model trained on satellite data really find brambles on the ground?
- cuno 1y agoSo 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?
- Waterluvian 1y agoI 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.
- CrazyStat 1y ago> 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?
- Waterluvian 1y agoEvery 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.
- bluGill 1y agoIt is complex. I was going to write out how it works in a simple way that everyone could understand - but then I realized that even though it would be a gross simplifications that are unrealistic, it still would be so complex that people would go "yep I understand that to every step", and then finish and not understand it. Every step alone makes perfects sense and is simple, but the total quickly gets complex. Even calling this a speculative market is a gross simplification of the truth.
- wbl 1y agoIt is good to enable people to hedge against bad harvests.
- bluGill 1y agoThere are two sides hedging against bad harvests, the farmer that grows the crop, and the industry (cattle, ethanol, food oils, and others) that buys that crop. The farmer wants to get paid, and the industry wants to get their crop.
- sadiq 1y agoHyperspectral 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 https://github.com/ucam-eo/geotessera
- sadiq 1y agoYes! 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.
- 0_____0 1y agoI've wondered this about finding hot springs.
- avsm 1y agoThat'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 https://crank.recoil.org/w/mDzPQ8vW7mkLjdmWsW8vpQ and the source https://github.com/ucam-eo/tessera-interactive-map https://github.com/ucam-eo/tessera-interactive-map
- avsm 1y agoYes 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 Because the embeddings are precomputed, the library just has to download the tiles from our server. More at: https://anil.recoil.org/notes/geotessera-python https://anil.recoil.org/notes/geotessera-python 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 https://github.com/ucam-eo/tessera-interactive-map The berries were a bit sour, summer is sadly over here!
- throwup238 1y agoThis 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.
- tony_cannistra 1y agoalmost definitely!
- sadiq 1y agoIt 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.
- emorning4 1y ago[dead]
- pbhjpbhj 1y agoNot 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.
- sadiq 1y agoHi! You can find a bit more about Gabriel's model through some of his posts over the last few weeks: https://gabrielmahler.org/posts/ https://gabrielmahler.org/posts/ When it comes to the satellite images, the model actually used TESSERA (https://arxiv.org/abs/2506.20380 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.
- whalesalad 1y agoFarmLogs (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.
- jcims 1y agoSeems like it could be pretty useful for archaeology as well.
- sadiq 1y agoThat'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.
- spogbiper 1y agoRelated, I think? Satellite + AI = finding things, not sure if similar beyond that https://www.pnas.org/doi/10.1073/pnas.2407652121 https://www.pnas.org/doi/10.1073/pnas.2407652121
- folli 1y agoAs 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 https://github.com/r-follador/delta-relief It would be interesting to overlay TESSERA data there, although the resolution is of course very different.
- siva7 1y agocan it find me truffles?
- sadiq 1y agoIf you have some GPS locations of truffles, you could use the notebook Anil mentioned here https://news.ycombinator.com/item?id=45378855 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.
- ggm 1y agoIf 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.
- thinkingemote 1y agoBrambles are blackberries. Sloes are from Blackthorn bushes. They are different plants but probably are in the same location!
- cjensen 1y agoThe in-person verification of hotspots was good, but in-person verification of non-hotspots was not done, and might be difficult.
- NedF 1y ago> 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, it would be more believable. What they are asking is impossible, increasing the likelihood without silly hacks like it's not in rivers or on top of buildings is an interesting problem but out of scope for academics.
- dmbche 1y agohttps://gabrielmahler.org/environment/ai/ml/%F0%9F%A6%94/2025/08/07/summer5.html 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"
- xarope 1y agoisn't this the same findings as the old "we trained to identify huskies, but instead we identified snow" problem?
- sadiq 1y agoI was a lot more optimistic about Gabriel's model than he was. It is essentially a presence-only species distribution model where accuracy depends largely on assumptions around prevalence and which really needs some presence-absence data to calibrate. 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.
- daemonologist 1y agoThe whole-earth embeddings are interesting. Wonder if it'd be any good for looking for fresh water sources in the desert.
- avsm 1y agoAre you thinking of _new_ fresh water sources that emerge in recent years? If you have any candidate lat/lon where this might have happened, we can take a look at the 2024 and earlier embeddings to see if we can spot it.
- lightedman 1y agoA model I have trained on ASTER and LANDSAT data has major difficulties identifying spots for agate hunting. Even after I've given it extra instruction such as looking only in volcanic terrain (with USGS map provided,) or focusing on mixed signals of hydrous silica and iron, checking near known fault zones in said volcanic areas, it still gave me results everywhere, and almost none matching my criteria. Plants are a way different and more difficult ballgame (they like to mess up my satellite data) so as I read I am not surprised to see that this didn't really give proper results.
- themafia 1y ago> So it turns out that there's a lot of bramble between the community center and entrance to Milton Country Park. > In every place we checked, we found pretty significant amounts of bramble. [Shocked Pikachu face]
- uwcs 1y ago> Stopping to take a photo of a very photogenic bee Show us the bee!
- avsm 1y agohttps://www.inaturalist.org/observations/316277035 https://www.inaturalist.org/observations/316277035, needs some id help :-)
- Peteragain 1y agoI read this and questioned the statistical methods 101. To say it works, one would also need to check for false positives. And such a check would pick up on "oh it's finding roads and there's a correlation between road and brambles."
- lloeki 1y agoWell 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.
- sadiq 1y agoWe did note several places during the trip that didn't contain bramble. The hotspot in the middle of the residential area was also entirely isolated. 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).
- ensocode 1y agoWe 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 1y agoI would try https://github.com/ucam-eo/tessera-interactive-map 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 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 1y agoThanks 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 1y agoSo 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.