I’ve got a satellite dataset (Sentinel 2) that contains data of various spectral bands for a specific, large area. Within this area are polygons, defined using a shapefile, for which I have the classes.
I am able to mask out the polygons from the various band rasters into numpy arrays using rasterio and geopandas. However, what I’m stuck with right now, is how to convert these arrays into a proper format for a muti-class supervised learning problem. The main issue being the variable size and orientation (imagine shapes like / | \ _ - ~).
Using minimum bouding box is certainly an option, but there is a very high chance of capturing adjacent polygons with this method, which could likely product bad performance.
Looking forward to hearing what you guys think.
P.S First post on the forums, so welcome any critique.