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The airtiler generates training / test data for neural networks by downloading buildings from vector data from OpenStreetMap and the corresponding satellite images from Microsoft Bing Maps.

It then generates binary masks from the vector data which can be used for example for instance segmentation.

Examples

Instance Separation

Image

Mask

False

image1

image2

True

image3

image4

Installation

To install airtiler run:

pip install airtiler

Usage

airtiler -c sample_config.json

API

airtiler = Airtiler("bing_key")
airtiler.process(config)

Config

Key

Required

options

boundingboxes

Yes

Options (optional)

Key

Description

target_dir

The directory where the files will be written to

zoom_levels

Global zoom levels which will be used, if a boundingbox if specified in short format or has no boundingboxes.

separate_instances

If true, each building instance will be separated. Otherwise, a building consisting from multiple instances will be rendered as one.

Sample config

{
  "options": {
    "target_dir": "./output/blabla",
    "zoom_levels": [15, 16, 17],
    "separate_instances": false
  },
  "query": {
    "tags": ["highway", "building", "leisure=swimming_pool"]
  },
  "boundingboxes": {
    "firenze": [11.239844, 43.765851, 11.289969, 43.790065],
    "rapperswil": {
      "zoom_levels": [17, 18],
      "tr": 8.818724,
      "tl": 47.222126,
      "br": 8.847435,
      "bl": 47.234629
    },
    "new_york": {
      "tr": -74.02059,
      "tl": 40.646089,
      "br": -73.864722,
      "bl": 40.77413
    }
  }
}

Projects

The airtiler is used in the following projects:

Metadata

Release files for airtiler 2.1.7

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

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Source distribution for airtiler 2.1.7
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