| ▲ | NedF 9 hours 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. | ||
| ▲ | sadiq 4 minutes ago | parent | next [-] | |
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. | ||
| ▲ | dmbche 6 hours ago | parent | prev | next [-] | |
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 3 hours ago | parent | prev [-] | |
isn't this the same findings as the old "we trained to identify huskies, but instead we identified snow" problem? | ||