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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
by adamwkraft 11y ago
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?
- bbabenko 11y agoTacking on a shameless recruiting plug: if you're interesting in computer vision, deep learning, and/or satellite imagery, Orbital Insight is hiring! http://orbitalinsight.com/jobs/machine_vision.html http://orbitalinsight.com/jobs/machine_vision.html
- bradneuberg 11y ago[Cloudless developer here] Hi Boris! It's Brad from Dropbox :) Responding to the parent comment, in terms of running time, the primary bottleneck is the RCNN localization portion; that takes about a minute and a half on my laptop to process a single image. In the Future Work section of the blog post I talk about collapsing the entire detection and localization pipeline into a single deconvolution network that directly takes in raw images and outputs image masks. The hope is that this runs much faster at inference time.