:py:mod:`kwcoco.data.grab_spacenet`
===================================

.. py:module:: kwcoco.data.grab_spacenet

.. autoapi-nested-parse::

   .. rubric:: References

   https://medium.com/the-downlinq/the-spacenet-7-multi-temporal-urban-development-challenge-algorithmic-baseline-4515ec9bd9fe
   https://arxiv.org/pdf/2102.11958.pdf
   https://spacenet.ai/sn7-challenge/



Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   kwcoco.data.grab_spacenet.grab_spacenet7
   kwcoco.data.grab_spacenet.convert_spacenet_to_kwcoco
   kwcoco.data.grab_spacenet.main



.. py:function:: grab_spacenet7(data_dpath)

   .. rubric:: References

   https://spacenet.ai/sn7-challenge/

   Requires:
       awscli


.. py:function:: convert_spacenet_to_kwcoco(extract_dpath, coco_fpath)

   Converts the raw SpaceNet7 dataset to kwcoco

   .. note::

      * The "train" directory contains 60 "videos" representing a region over time.
      
      * Each "video" directory contains :
          * images           - unmasked images
          * images_masked    - images with masks applied
          * labels           - geojson polys in wgs84?
          * labels_match     - geojson polys in wgs84 with track ids?
          * labels_match_pix - geojson polys in pixels with track ids?
          * UDM_masks - unusable data masks (binary data corresponding with an image, may not exist)
      
      File names appear like:
          "global_monthly_2018_01_mosaic_L15-1538E-1163N_6154_3539_13"


.. py:function:: main()