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        "description": "Airtiler\n========\n\n|Build Status|\n\nThe airtiler generates training / test data for neural networks by\ndownloading buildings from vector data from OpenStreetMap and the\ncorresponding satellite images from Microsoft Bing Maps.\n\nIt then generates binary masks from the vector data which can be used\nfor example for instance segmentation.\n\nExamples\n--------\n\n+---------------------+----------+----------+\n| Instance Separation | Image    | Mask     |\n+=====================+==========+==========+\n| False               | |image1| | |image2| |\n+---------------------+----------+----------+\n| True                | |image3| | |image4| |\n+---------------------+----------+----------+\n\nInstallation\n------------\n\nTo install airtiler run:\n\n.. code:: python\n\n    pip install airtiler\n\nUsage\n-----\n\n::\n\n    airtiler -c sample_config.json\n\nAPI\n~~~\n\n.. code:: python\n\n    airtiler = Airtiler(\"bing_key\")\n    airtiler.process(config)\n\nConfig\n~~~~~~\n\n+---------------+----------+\n| Key           | Required |\n+===============+==========+\n| options       |          |\n+---------------+----------+\n| boundingboxes | Yes      |\n+---------------+----------+\n\nOptions (optional)\n^^^^^^^^^^^^^^^^^^\n\n+-----------------------------------+-----------------------------------+\n| Key                               | Description                       |\n+===================================+===================================+\n| target_dir                        | The directory where the files     |\n|                                   | will be written to                |\n+-----------------------------------+-----------------------------------+\n| zoom_levels                       | Global zoom levels which will be  |\n|                                   | used, if a boundingbox if         |\n|                                   | specified in short format or has  |\n|                                   | no boundingboxes.                 |\n+-----------------------------------+-----------------------------------+\n| separate_instances                | If true, each building instance   |\n|                                   | will be separated. Otherwise, a   |\n|                                   | building consisting from multiple |\n|                                   | instances will be rendered as     |\n|                                   | one.                              |\n+-----------------------------------+-----------------------------------+\n\nSample config\n~~~~~~~~~~~~~\n\n.. code:: json\n\n    {\n      \"options\": {\n        \"target_dir\": \"./output/blabla\",\n        \"zoom_levels\": [15, 16, 17],\n        \"separate_instances\": false\n      },\n      \"query\": {\n        \"tags\": [\"highway\", \"building\", \"leisure=swimming_pool\"]\n      },\n      \"boundingboxes\": {\n        \"firenze\": [11.239844, 43.765851, 11.289969, 43.790065],\n        \"rapperswil\": {\n          \"zoom_levels\": [17, 18],\n          \"tr\": 8.818724,\n          \"tl\": 47.222126,\n          \"br\": 8.847435,\n          \"bl\": 47.234629\n        },\n        \"new_york\": {\n          \"tr\": -74.02059,\n          \"tl\": 40.646089,\n          \"br\": -73.864722,\n          \"bl\": 40.77413\n        }\n      }\n    }\n\nProjects\n--------\n\nThe airtiler is used in the following projects:\n\n-  `Deep OSM - Instance segmentation using orthophotos and OSM data`_\n\n.. _Deep OSM - Instance segmentation using orthophotos and OSM data: https://github.com/mnboos/osm-instance-segmentation\n\n.. |Build Status| image:: https://travis-ci.org/mnboos/airtiler.svg?branch=master\n   :target: https://travis-ci.org/mnboos/airtiler\n.. |image1| image:: https://github.com/mnboos/airtiler/blob/master/images/image2.png\n.. |image2| image:: https://github.com/mnboos/airtiler/blob/master/images/mask2.png\n.. |image3| image:: https://github.com/mnboos/airtiler/blob/master/images/image1.png\n.. |image4| image:: https://github.com/mnboos/airtiler/blob/master/images/mask1.png",
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