Tweetovi
- Tweetovi, trenutna stranica.
- Tweetovi i odgovori
- Medijski sadržaj
Blokirali ste korisnika/cu @MaxLenormand
Jeste li sigurni da želite vidjeti te tweetove? Time nećete deblokirati korisnika/cu @MaxLenormand
-
Prikvačeni tweet
Hi Twitter! Nearly graduated, I’m looking for my 1st job! I’m a 23 y.o.
#Aerospace#Engineer, but also a lot into#GIS &#DataScience. I’d love to work in computer vision applied to#RemoteSensing. Free starting Feb/March 2020, anywhere in the
!
RT would be awesome!
pic.twitter.com/gdGR90C0Yh
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
I can understand that people might not understand why some of us care about our privacy & go out of our way to prevent our data from being collected.
@PhilosophyTube isn't a tech guy, and it's great that more people outside of the field talk about it too:https://www.youtube.com/watch?v=fCUTX1jurJ4 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
After joining
@kaggle, the biggest competitive data science platform out there about a year ago, I finally reached the expert ranking! Learned a lot & keep learning every day on there, awesome tool to keep up to date with the latest research & how to apply itpic.twitter.com/WyLrpjNEQH
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Me: Ah damn it I'm so tired I don't feel like writing my internship report, takes too much brain power... Also me: Ooooouuh a really interesting state of the art 15 page paper on deep learning, let's read it multiple times!
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Oh, and I should mention, I'm also looking for a job! If you're looking for someone with machine learning / deep learning / computer vision experience applied to satellite imagery or anything else, you just read what I did for the past ~6 months!pic.twitter.com/CbxgKYZft9
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
I hope this was interesting to follow, and actually makes sense. Cutting down into tweets is far from easy, but a nice exercise. If you have any questions, feel free to reach out!
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Again, I won't go into detail about how this is done. For those interested, we use a UNet (done on Keras by the way, poke
@fchollet). This is the result (trained on Ghana, deployed in Nigeria).pic.twitter.com/ZQ01M1jJPs
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
I also ended up being the person with the most experience in computer vision using deep learning (those longs nights on
@kaggle have been useful) in the company. So I was tasked on deploying a model made externally on high resolution image to find palm trees.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
This isn't perfect, far from it. We can see some misses as well as some incorrectly labelled trees. But to know the shade management of a field, this is pretty damn good, especially using free image.
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The result is shown here in transparent above the higher resolution image. White pixels have been classified as shade trees. Keep in mind this is done ONLY with 10m resolution imagery, NOT the image used as reference.pic.twitter.com/8h5l9vglMM
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Here is what the input Sentinel 2 (10m resolution remember) image might look like (1st image). 2nd image is a much higher resolution image of the same area, so you can actually see where the shade trees are.pic.twitter.com/RVSMJePQ7M
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
In short, I give a satellite image, the location of some trees I know as well as some fields who's shade management I know (this is called the training data in machine learning); and I get a nice (hopefully) map of all the areas where shade trees are supposed to be.
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
I'm gonna detail what I came up with in the end, Twitter isn't suited for this. But for those who might care, and just because I think this looks pretty cool (and I'm proud of it), this is what my last model looks like. (Feel free to reach out / @ me if you have any questions!)pic.twitter.com/rXTEh0037Y
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Okay, obviously this is trickier than I expected. But it's also really interesting! Most computer vision problems tackle objects that take up a large area in the image, rarely 1 or even less than 1 pixel. So there's a lot of interesting and novel things here!
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Then I can threshold this (every proba > threshold becomes a tree, everything < becomes not-a-tree). This looks nice, but remember we're looking for scattered trees, and here we are seeing big patches. That's still not satisfying. (For those who care, this gets a 0.3 F1 score)pic.twitter.com/vLBeloVjiR
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
I then feed this into a model that is going to look at every pixel and give it a probability based on how close it looks to all of these pixels I marked in orange previously. Lower left there's a city (masked out) with low proba around it. Makes sense: no trees around the city.pic.twitter.com/XTh9wyU8zc
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The next idea, was trying to make a model find those patterns by itself. So I took my sharpest digital pen (aka a mouse) and started marking all the places where I, the human, could see shade trees. (Original image (cloud free Sentinel 2 + Pleiades under) and marked image)pic.twitter.com/NY69cDArH5
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Getting there, 61.5% accuracy. But let's be real, that's not usable. Nobody wants something that is right 6 times out of 10, or more importantly wrong 4 times out of 10. So I move on.
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
But that was only with 1 image! So my little brain goes on thinking and maybe trees have a different property over a year than the rest of the field? I decide to use all the cloud free images I have for 2018 and try again:pic.twitter.com/SGim5jJhLd
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The most important number is the one in the red box: 55.5% accuracy. That means, if you give me a random crop of image, something like the picture here, my model can tell you 55 times out of 100 correctly if it's a shade tree or not. At random, accuracy is 50%. Not great.pic.twitter.com/FXlkyN0NlR
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
In short the answer is no. I tried a bunch of stuff (GLCM study for those who care) and I plugged those in a machine learning algorithm (funny to use a Random Forest when looking for trees by the way), and these are the results:pic.twitter.com/6wEwSAzioJ
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
Čini se da učitavanje traje već neko vrijeme.
Twitter je možda preopterećen ili ima kratkotrajnih poteškoća u radu. Pokušajte ponovno ili potražite dodatne informacije u odjeljku Status Twittera.