Images_to_samples.py (hdf5s) --> data_to_tiles.py (tiles as tifs and geojsons)

[remtav]

Features

• CRS mismatch automatic handling
• Resize image to new spatial resolution (ex.: 50cm to 25cm)
• Don't rewrite tiles for an image that has all expected tiles
• Crop geopackage to geojson (don't burn right away)
• Input a csv directly (rush mode)
• Input an image directly (for inference if pre-tiling is preferred?)
• Parallelize tiling

Where to write tiles?

data_path / experiment_name / samples_folder_name ?
ex.:
geo_deep_learning/data/geobase_buildings/tiles_3bands_minannot1

How to deal with CRS mismatch?

• to be discussed...

Onboarding solaris

• How to integrate necessary solaris' utils?
• 1st attempt

[remtav]

Suggested logic for data_to_tiles.py

[remtav]

Suggested class structure for use in data_to_tiles.py

[remtav]

Seeing comments from team members on Slack and Github, seems like this issue needs further discussion.
I suggest starting from a pros/cons table, then discussing it in a meeting based on this table.

Tiling approach	Pros	Cons
HDF5	*
Plain .geotiff/.geojson

*This type of table that expects a list of items is much easier done with Google Doc than Markdown

@victorlazio109 , @mpelchat04 , @ymoisan , @CharlesAuthier, please add/review the following Google Doc "Pros and Cons" table. This will serve as a basis for discussing the relevance of this PR and overall transition away from HDF5s.