Using only the 2016-2017 data is very limiting because it is only from mid-summer. I wouldn't expect models trained on just this data to generalize to other times of year, and indeed we have seen substantial performance drops on images from other times of year. We have data from all seasons from 2012-2014. It is formatted differently but contains roughly the same information. Putting these datasets together and training on the result is the lowest-hanging fruit for providing more value with this project.