Georeferenced dataset post-processing

This release essentially copes with the georeferenced dataset, one may now post-process
the results, so as to visualize labelled masks as raster. A vectorized version of each
prediction is also available.

As another major evolution, deeposlandia now has a Command-Line Interface (CLI). The
available commands are datagen, train, infer and postprocess respectively for
generating preprocessed datasets, training neural networks, doing inference and
post-processing neural network outputs.

Added

• Set up a Command-Line Interface (#90).
• Consider RGBA images and warns the user as this format is not handled by the web app
  (#107).
• Consider geometric treatments in a dedicated module, add vector-to-raster and
  raster-to-vector transformation steps ; save postprocessed images as vector and raster
  files (#119).
• Postprocess aerial images so as to produce predicted rasters (#118, #126, #127).
• Add missing test files for Tanzania dataset.
• Some information about GDPR in the web app (#113).
• Improve unit tests dedicated to georeferenced data processing (#104).

Changed

• Label folders are standardized (labels), in particular this folder name replaces gt
  for Aerial dataset (#139).
• Always use the best existing model, instead of parametrizing the access to the model
  (#135).
• Broken images are considered, hence not serialized onto the file system (#129).
• The georeferenced aerial datasets are updated and factorized into a generic
  GeoreferencedDataset class, the test files are updated accordingly (#128).
• Deep learning model are now known as featdet and semseg instead of
  feature_detection and semantic_segmentation (#133).
• Update the training metric history when using a existing trained model (#102).
• Move the documentation to a dedicated folder.
• Some code cleaning operations, using black and flake8 (#120).
• Update dependencies, especially Tensorflow, due to vulnerability issues.
• Fix the unit tests for Tanzania dataset population (#111).
• The process quantity is an argument of populate() functions, in order to implement
  multiprocessing (#110).
• Logger syntax has been refactored (%-format) (#103).

Removed

• The concept of "agregated dataset" is removed, as we consider a home-made Mapillary
  dataset version. As a consequence, some input/output folder paths have been updated
  (#134).
• The hyperparameter optimization script (paramoptim.py) has been removed, train.py
  can handle several value for each parameter (#125).