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This is neat. We've deployed a 98+% accuracy satellite & aerial cloud detection solution for many years, so I would have the following suggestions: -Why use A

This is neat. We've deployed a 98+% accuracy satellite & aerial cloud detection solution for many years, so I would have the following suggestions: -Why use AlexNet and not VGG (or Googlenet)? -Make sure to train on clouds vs desert. There are a lot of instances where their spectral signatures are very close, depending on the satellite. -Make sure to train on clouds vs snow. They are even more close. -Dark clouds. This might not show up much unless you're working with the satellite vendor, but there are cloud formations where shadows of clouds project onto other clouds. Very difficult to deal with and NN may be well suited to it. I would say since it's absolutely possible to get higher accuracy using older methods on this exact satellite constellation, there is definitely room for improvement. Just switching to VGG might even do the trick. But this is a great first step!

gus_massa 11y ago
I'd add salines. My mother allways says that she and her team was very confused about a static "cloud" over Bolivia until they realize it was a saline. It was a long time ago, using the firsts satellite images, so they had less resolution and probably only the visible spectrum. Example: https://www.google.com/maps/@-19.8339409,-67.5366835,253390m/data=!3m1!1e3 https://www.google.com/maps/@-19.8339409,-67.5366835,253390m...
bradneuberg 11y ago
[Cloudless developer here] Thanks for all the great suggestions! In terms of your existing 98% accuracy solution, can you point me to more details if possible? I grabbed AlexNet as its a bit easier to work with and was readily available as a fine tunable model on the Caffe Model Zoo, but you're certainly right that VGG or Googlenet would give more accuracy. For training on clouds vs snow and deserts, this is a bit more of a proof of concept for now based on the data we had access to (state of California). Someone could certainly scale this up using a larger data set and generating more annotation data with examples of snow and deserts to handle more edge conditions. They would probably want to build Mechanical Turk support into the annotation tool if they did. Thanks for all the great comments! All of this is open source so contributions using any of these suggestions are certainly possible. Best, Brad Neuberg
- strebler 11y ago
  We did it before Deep Learning existed. If we had to do it again today from scratch, I think we'd use an approach similar to yours. My comment about VGG was based on experience with VGG vs AlexNet in other projects.

gus_massa 11y ago

I'd add salines. My mother allways says that she and her team was very confused about a static "cloud" over Bolivia until they realize it was a saline. It was a long time ago, using the firsts satellite images, so they had less resolution and probably only the visible spectrum. Example: https://www.google.com/maps/@-19.8339409,-67.5366835,253390m/data=!3m1!1e3 https://www.google.com/maps/@-19.8339409,-67.5366835,253390m...

bradneuberg 11y ago

[Cloudless developer here] Thanks for all the great suggestions! In terms of your existing 98% accuracy solution, can you point me to more details if possible? I grabbed AlexNet as its a bit easier to work with and was readily available as a fine tunable model on the Caffe Model Zoo, but you're certainly right that VGG or Googlenet would give more accuracy. For training on clouds vs snow and deserts, this is a bit more of a proof of concept for now based on the data we had access to (state of California). Someone could certainly scale this up using a larger data set and generating more annotation data with examples of snow and deserts to handle more edge conditions. They would probably want to build Mechanical Turk support into the annotation tool if they did. Thanks for all the great comments! All of this is open source so contributions using any of these suggestions are certainly possible. Best, Brad Neuberg

strebler 11y ago
We did it before Deep Learning existed. If we had to do it again today from scratch, I think we'd use an approach similar to yours. My comment about VGG was based on experience with VGG vs AlexNet in other projects.