Create a dataset with random sampling

[elboyran]

The new datastore should be of images/tiles of resolution 80m = 100px. All slum pixels should be used to test of a tile contains at least 80% slum pixels. If yes => use for training, if no, check if it has at least 80% pixels of that class. If yes => use for training, if not- discard from training. See how many slum tiles are obtained above. Randomly sample the same (remaining) number of pixels/80% tiles of the remaining 2 classes- BuiltUp and NonBuiltUp.

• [x] Create functions and scripts to do the 'slum conditional' tiling.

• [x] Generate training image tiles of the 3 classes.

• [x] Clean up by hand some of the bad training images.

• [x] Record the new dataset in the Excel sheet.

[elboyran]

After the automatic generation there are 11 914 images per class.

[elboyran]

After the manual clean up there are 10 000 image tiles for class Slum; 10 800 BuiltUp. and 11 090 NonBuiltUp.