Using Convolutional Neural Networks to Automatically Analyze Aerial and Satellite Imagery

In this recording of our most recent Technical Staff Meeting, Lewis Fishgold walks us through the team's work on Raster Vision, a set of open source tools for automatically analyzing aerial and satellite imagery using convolutional neural networks.

Outline:
* a review of convolutional neural networks
* our approaches to tagging and semantic segmentation for two machine learning contests
* demo of a tool for visualizing output on an interactive map. [Seeing the results on a map can give a great sense of where the algorithms get it right, and where they get it wrong, and where they amusingly have a tough time figuring it out (e.g. a large food truck: is it a car or a building?)]
* our use of AWS Batch for running experiments
* a preview of some new work on object detection

Read more about this work in this blog post:
https://www.azavea.com/blog/2017/05/30/deep-learning-on-aerial-imagery/

Machine Learning Contest Leaderboards:
http://www2.isprs.org/potsdam-2d-semantic-labeling.html
https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/leaderboard

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