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I hacked together a solution for this and wrote a blog article a while back on a very similar topic, which I will summarize here. The script is intended to extract a river from a 4-band NAIP image using an image segmentation and classification approach. Convert image to a numpy array Perform a quick shift segmentation (Image 2) Convert segments to raster ...


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You could look at clustering in scikit-learn. You will need to read the data into numpy arrays (I'd suggest rasterio) and from there you can manipulate the data so that each band is a variable for classification. For example, assuming you have the three bands read into python as red, green, and blue numpy arrays: import numpy as np import sklearn.cluster ...


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Your last attempt looks very promising. With more than 5 points you might get an even better picture. Since the image covers Minnesota, my first guess would be to use one of the CRS defined for that state. So go to Project -> Project Properties, CRS tab, and enter Minnesotain the search field. All results are using Lambert Conformal Conic projection. ...



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