Hello @Lolletti
For the afghanisthan region, training data from April may not be sufficient (atleast for me. Getting ~80% accuracy). This makes sense because in this period most of the crops are harvested, which doesn't help in classification.
And are the labels are credible for the afg region for the train and test period. Because there is huge perfomance gap with respect to other regions.
If the labels were collected during different time periods....then the modelling only for April is not useful idea. Same is true for the test dataset.
I was wondering how the top teams are approaching the AFG region modelling.
Thanks
I have exactly 80% accuracy for Afg too, difficult to go higher than that
Thanks for your query. I confirm that data were collected in April 2022 and are based on visual interpretation of VHR images. Participants are allowed to increase the test dataset with open source data. Considering the challenge represented by the limited time window, 80% of accuracy isn't that bad in my personal opinion.
I thinks it's train data instead of test data that we can collect.
Yes yes, I meant training data.... I apologize, thanks for correcting
Thank you.