adamwkraft

Very cool stuff! We have a lot of internal tools for automatically detecting clouds at Orbital Insight as clouds present a large challenge across many types of satellite resolutions and bands. Glad to see more people are turning towards deep learning for satellite imagery.

Curious to hear how long the pipeline takes to run on a single image? Also, have you thought about running the pipeline in a fully convolutional manner, reducing the need for proposal regions?

I want to share some quick results I had from trying out the dataset. First off, I want to say thanks for your work and the dataset. I'm glad to see more people getting excited about machine learning in this field!

I used a simple Lenet-like architecture to train both Sat4 and Sat6. This seemed reasonable, especially considering this network works well on the 28x28 patches for MNIST. After only 10k iterations, I achieved accuracies of 98% on Sat4 and 97% on Sat6. I haven't gone back yet, but I assume these results could be increased with tweaking the network slightly or playing around with learning parameters. I also spent a while looking for any bugs in my evaluation, but things seemed to check out. This would suggest that CNN may be able to at least match, if not potentially outperform (97% vs 94% on Sat6) the methods in the paper. I'm curious to hear if anyone else has gotten similar results with CNN or other methods? I'm also curious to know if you tried other CNN architectures other than those described in the paper?