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lifecycle License: MIT

README | English | 简体中文 |

Historical OpenStreetMap Objects to Machine Learning Training Samples

The ohsome2label offers a flexible label preparation tool for satellite machine learning applications.

  • Customized Object - user-defined geospatial objects are retrieved and extracted from OpenStreetMap full-history data by requesting ohsome web API.
  • Various Satellite Images - user could downloads corresponding satellite imagery tiles from different data providers.
  • Seamless Training - object labels together with images would be packaged and converted to Microsoft COCO .json format to provide a seamless connection to further model training.
  • OSM Quality Measurement (currently under developing) - historical OpenStreetMap analysis allows us to provide exclusive intrinsic quality measurements in addition to the customized training samples.

The output package could support directly training of popular machine learning tasks (e.g., object detection, semantic segmentation, instance segmentation etc,).

Package Dependencies

  • python 3.6


pip install ohsome2label

or clone the Github repo and install it locally

pip install --editable .


We believe you are familiar with OpenStreetMap, otherwise you are highly recommended to check this out since OpenStreetMap contains a huge types of geographical data, ranging from highways to different buildings. Before you start playing around with ohsome2label, you have to specify parameters, such as the target bounding box or the geospatial object you are looking for.

Here is a example for config.yaml file.

  name: HD_landuse
  workspace: ./example_result
  project_time: 2020-05-18
  task: segmentation

  api: ohsome
  bboxes: [8.625,49.3711,8.7334,49.4397]
    - {'label': 'urban', 'key': 'landuse', 'value': 'residential'}
    - {'label': 'urban', 'key': 'landuse', 'value': 'garages'}
    - {'label': 'industry', 'key': 'landuse', 'value': 'railway'}
    - {'label': 'industry', 'key': 'landuse', 'value': 'industrial'}

  timestamp: 2019-10-20
  types: polygon

  img_api: bing
  api_token : 'YOUR OWN API TOKEN'
  zoom: 16
Scheme Parameter Description
project name The title of project you want to have.
project workspace The relative path your want to store the project output.
project project_time The created time of the project.
project task The type of machine learning tasks you would like to use afterwards. This could be object detection, segmentation.
osm api The OSM data query API, which could be ohsome, overpass.
osm url The corresponding URL for OSM data query API , which could be,
osm bboxes The box boundary of target area, which is given in form [xmin, ymin, xmax, ymin], x and y refer to longitude and latitude. Longitude ranges from -180 to 180, and latitude ranges from 0 to 90. The default map project is WGS84.
osm tags The target key and values pairs of OSM feature, where label could be defined by yourself and shared by several key-value paris. A valid OSM key is necessary, where OSM values could be empty means all values are included.
osm timestamp The timestamp of historical OSM data you want to retrieval. The date should be given in [year-month-day]
osm types The object types you are aimed at, which could be polygon, line.
image image_api The satellite imagery service you would like to use. Now bing,mapbox, sentinel are supported. Also, custom TMS is also supported
image image_url The url template of satellite imagery service you would like to use.
image api_token The API token should be applied individually by users. Please find the corresponding application pages as follows: bing, mapbox, sentinel
image zoom The zoom-in level of satellite imagery. This 'zoom level' would affect the spatial resolution in general.

Command line functions

Once the config.yaml is well prepared, now ohsome2label would help your to prepare your customized OSM training samples by using the following command line functions. Please notice, you could define your own config.yaml file for all command line functions, for instance ohsome2label --config PATH/OWN_config.yaml vector


Check out the summary of ohsome2label command line function with the following command.

$ ohsome2label --help
Usage: ohsome2label [OPTIONS] COMMAND [ARGS]...

 Generate training label for deep learning via ohsomeAPI

 -v, --verbose
 --config PATH
 --schema PATH
 --help         Show this message and exit.

 image      Download satellite image
 label      Generate tile
 printcfg   Print project config
 quality    Generate OSM quality figure
 vector     Download vector OSM data from ohsomeAPI
 visualize  Visualize of training samples


Download the historical OpenStreetMap vector data with the given timestamp by querying the ohsome API. The results is in geojson format.

$ ohsome2label vector
  -v, --verbose
  --config PATH
  --schema PATH
Download OSM historical data into dir:



The downloaded OpenStreetMap data together with the target objects would be labelled with specific zoom level. The label output also depends on different ML_task, for examples, object bounding boxes for object detection, object footprints for semantic segmentation, and instance footprint for instance segmentation.

$ ohsome2label label
  -v, --verbose
  --config PATH
  --schema PATH

Tile the OSM data into given zoom level: 14

24it [00:00, 119.13it/s]


Based on the previous label results, user could download the correspondingly satellite image for training.

Templates of image_url for different image_api:

  • For bing:{q}.png?g=854&mkt=en-US&token={token}
  • For mapbox:{z}/{x}/{y}.jpg?access_token={token}
  • For sentinel:{token}?showLogo=false&service=WMS&request=GetMap&layers=ALL-BAND&styles=&format=image%2Ftiff&transparent=1&version=1.1.1&maxcc=20&time=2015-01-01%2F2020-01-01&priority=mostRecent&height=256&width=256&srs=EPSG%3A3857&bbox={bbox}
  • For custom URL: only support x, y, z and token in image_url
$ ohsome2label image 
  -v, --verbose
  --config PATH
  --schema PATH
Start download satellite image!
100%|███████████████████████████████████| 24/24 [00:03<00:00,  6.57it/s]


Visualize example satellite image together with OpenStreetMap features. Requires the /tile folder from the previous step. Accepts an additional flag:

  • -n or --num: integer number of examples images to create per class. (default: 50)
  • -t or --type: str the visualization type should be selected from combined or overlay (default: combined)
$ ohsome2label visualize -n 10
  -v, --verbose
  --config PATH
  --schema PATH
start visualize 10 pictures!

Visualization mode: combined the satellite image with OpenStreetMap features.

With the default Heidelberg example, you would get the following training samples for the landuse classes: urban and industry.


Generate intrinsic quality indications based on Historic OSM data, which give a insight into the intrinsic quality of OSM training samples.

ohsome2label quality
  -v, --verbose
  --config PATH
  --schema PATH
100%|███████████████████████████████████| 3/3 [01:48<00:00, 36.24s/it]

As a example for the default Heidelberg example, we hard-code three intrinsic quality indications: 1. density of OSM polygon features areas (area of polygon divided by the total area in square-kilometers); 2.density of OSM polygon features numbers (number of elements divided by the total area in square-kilometers); 3.density of OSM users (number of contributors divided by the total area in square-kilometers). In general, if the density of OSM features are getting stable, this could refer to a relatively complete mapping status. In the future, one may develop more sophisticated indicators based on specific “fitness-for-use” purposes.

Print the configuration

Users could use the print configuration to check their configuration regarding the project.

$ ohsome2label printcfg
  -v, --verbose
  --config PATH
  --schema PATH
# # # # # # # # # # #  CONFIG  # # # # # # # # # # #
{'_config': {'image': {'api_token': '', 'img_api': 'bing', 'zoom': 16},
             'osm': {'api': 'ohsome',
                     'bboxes': [8.625, 49.3711, 8.7334, 49.4397],
                     'tags': [{'key': 'landuse',
                               'label': 'urban',
                               'value': 'residential'},
                              {'key': 'landuse',
                               'label': 'urban',
                               'value': 'garages'},
                              {'key': 'landuse',
                               'label': 'industry',
                               'value': 'railway'},
                              {'key': 'landuse',
                               'label': 'industry',
                               'value': 'industrial'}],
                     'timestamp':, 10, 20),
                     'types': 'polygon',
                     'url': ''},
             'project': {'name': 'HD_landuse',
                         'project_time':, 5, 18),
                         'task': 'segmentation',
                         'workspace': './example_result'}}}

# # # # # # # # # # # #  END  # # # # # # # # # # # #


Wu, Zhaoyan, Li, Hao, & Zipf, Alexander. (2020).From Historical OpenStreetMap data to customized training samples for geospatial machine learning. In proceedings of the Academic Track at the State of the Map 2020 Online Conference, July 4-5 2020. DOI:

Raifer, M., Troilo, R., Kowatsch, F., Auer, M., Loos, L., Marx, S., Przybill, K., Fendrich, S., Mocnik, F.-B.& Zipf, A. (2019): OSHDB: a framework for spatio-temporal analysis of OpenStreetMap history data.Open Geospatial Data, Software and Standards 2019 4:3.


The package relies heavily on the OpenStreetMap History Database and the ohsome API. The idea of this package has been inspired by the nice work of label-maker. Last but not least, we would like to thanks for the contributions of OpenStreetMap volunteer to make this happen.

  • OpenStreetMap historical data used that contains ODbL 1.0 licensed OSM history data for dates after September 12, 2012 and CC-BY-SA 2.0 licensed OSM history data for all dates prior to September 12, 2012.