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.
Archives for September 2019
When it comes to the relationship between geospatial neural network performance and the amount of training data, do geographic differences matter?
When training a deep neural network to identify building footprints in satellite imagery, having more training data never hurts. But how much does more data help, and when is it worth the cost and difficulty of procuring it?
Using Solaris, you can fine-tune deep learning models pre-trained on overhead imagery for five minutes and achieve performance comparable to past SpaceNet Challenge prize-winners.
Now that the SpaceNet 5 dataset has been released, and the challenge is live on Topcoder, we anticipate a great many insights from this challenge into how well computer vision can automatically extract road networks and travel time estimates.