Thanks! This analysis is really helpful to sense check the accuracy of these kinds of ML predictions based on satellite data.
Out of interest, could you share any further insights around the challenges using the Sentinel-2 data set?
Out of interest, could you share any further insights around the challenges using the Sentinel-2 data set?
Not sure my observations on the Sentinel-2 data specifically are going to tell you anything you don't already know though - my original comment basically meant that identifying heterogenous and changing land use against heterogenous and changing surrounds on a regional scale is particularly hard when your spatial resolution is low enough for land areas being categorised to often only be a handful of pixels, and harder still when potential calibration metadata is older than the earliest images. We (my colleagues rather than me) solved our problem by incorporating other lower res and even radar datasets at the initial identification step and then having a manual verification stage using high res optical to evaluate the model output, but we were only identifying a few very specific rare types of land use. And again you're probably aware a model that's well fitted to one region might well needs recalibrating when used on regions with different topography and climate and typical land use patterns.