We need to design data products — artifacts and interfaces — that make data more accessible to less technical audiences. Good design gives us products that are accessible, informative and useful; even better design gives us experiences that are intuitive, compelling, and a pleasure to use.
Archives for 2019
We’re closing in on the final stretch for the SpaceNet 5 Challenge that aims to extract road networks and route travel times directly from satellite imagery. Yet there’s still plenty of time to get involved, as our previous blog showed reasonable road mask predictions after only 10 hours of training.
Deep learning models for interpreting satellite imagery show increasing performance as the amount of training data is increased. In this post, we’ll recreate our first analysis with a whole new model architecture, to see what changes and what stays the same.
In support of SpaceNet 5’s rather complex challenge, this post walks readers through the steps necessary to prepare the data for the first step in our baseline: creating training masks for a deep learning segmentation model.
It’s that special time of year when the days get shorter, the CPU temps run just a little cooler, and the bits are ripening on the binary search trees. Of course I mean that Hacktoberfest is finally here again.
When it comes to the relationship between geospatial neural network performance and the amount of training data, do geographic differences matter?