So 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?
Not 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?
FarmLogs (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.
> 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.
I 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.
A 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.
I 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."
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.
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?
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[ 2.6 ms ] story [ 46.4 ms ] threadWhat 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.
It would be interesting to overlay TESSERA data there, although the resolution is of course very different.
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.
For the "However, it is obvious that most of the generated findings aren’t brambles"
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.
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.
> In every place we checked, we found pretty significant amounts of bramble.
[Shocked Pikachu face]
Show us the bee!
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.