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[WSC19] Fitting the World: Physical Scale from Satellite Images

POSTED BY: William Goodall
8 Replies

Nice project! Last year at WSS18, a student did a very similar project: "Predicting the Scale of Satellite Images Using Neural Network." https://community.wolfram.com/web/community/groups/-/m/t/1379645

This is one of my favorite smaller NN projects I've seen. Good job.

I have a gut feeling this would be similar to estimating the fractal dimension of the binarized image.

https://community.wolfram.com/groups/-/m/t/1025046

A while back, another summer school student wrote a CNN to estimate fractal dimensions:

https://community.wolfram.com/groups/-/m/t/1140551 https://github.com/LordDarkula/FractalNet

It'd be really interesting to see to how strongly correlated the fractal dimension of the image and the scale are.

POSTED BY: Sean Clarke

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POSTED BY: Mohammad Bahrami
Posted 6 years ago

Super interesting research, and very good results given the scope and difficulty of the task. Especially love the idea on just having a neural network overfit the entire world :P Given that your end network architecture was tuned automatically, why do you think a "mostly linear" neural network ended up working best for this task?

POSTED BY: Nico Adamo

Because I was doing feature extraction separately, I only had to construct the classification half of the classic CNN image processing architecture. Also, in the limited time (and with the mid-tier GPU in my laptop) I had to train the net, having a smaller number of weights made training a little faster, not to mention less likely to overfit my data (in a bad way :) ).

POSTED BY: William Goodall
Posted 6 years ago

This is an amazing project! It seems like a fun idea to explore and it has many real world descriptions. I hope you continue to code in this wonderful style.

POSTED BY: Simeon Buttery

Thank you! It was definitely a lot of work organizing all the work---weaving a clear thread through all the various misadventures and failures was a challenge.

POSTED BY: William Goodall
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