Airtiler
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 |  |
 |
| True |  |
 |
Installation
To install airtiler run:
pip install airtilerUsage
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:
