Lopa & Lingo building footprints

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Lopa & Lingo building footprints is an import of building outlines in the Ituri province of the Democratic Republic of Congo (DRC).

Goals

Add building footprint data in the Lopa & Lingo area in Ituri province in DRC. Having building footprint data on OpenStreetMap (OSM) will provide additional detail for humanitarian organisations such as Médecins Sans Frontières (MSF) and others working in the area.

Schedule

The planning of the import started in March 2020, we would like to have the import completed in May 2020.

Import Data

Background

The two datasets are Esri shapefiles (.shp) created by the Department of Geoinformatcics - Z_GIS, University of Salsburg for MSF.

  • The data for Lingo is based on a WorldView-3 satellite image acquired on 01 January 2020 with 50 cm spatial resolution, provided by EUSI (European Space Imaging).
  • The data for Lopa is based on a WorldView-2 satellite image acquired on 15 January 2020 with 50 cm spatial resolution, provided by EUSI (European Space Imaging).

For both datasets buildings have been semi-automatically extracted using object-based image analysis and visual image interpretation. Building geometry from automated analysis has been regularised for homogeneous results. Automated extracted buildings were validated by visual image interpretation, whereby also missing buildings were added where needed. In the end, the complete dataset was again validated by an independent interpreter.

Data source site: The datasets are not online available. This import is set up by MSF staff who have access to the internal database.
Data license: Permission to share was given in a private email and states: "The provision of building footprint data (as created by our partner ZGIS) from the MSF Master database is fully in line with the data sharing dynamic that MSF wants to promote. In order to ensure the widest possible dissemination of this data, I am pleased to inform you that after running through the MSF internal geodata sharing policy, MSF agreed to share those non-sensitive data for OpenStreetMap under ODC-ODBL license. This under the condition that MSF and ZGIS will be mentioned on the OpenStreetMap wiki page as contributors to OpenStreetMap."

MSF approval

OSM attribution: Z_GIS & Médecins Sans Frontières
ODbL Compliance verified: Yes

OSM Data Files

Find here the source data files that we want to import. The link is temporary and will be removed once the import is finished.

Import Type

This is a one-time import, but the workflow of the import is designed to be launched every time we have a building footprint dataset created  by Z_GIS through the same methodology. Since the datasets are usually relatively small, we will use JOSM to upload the data onto OSM.

Data Preparation

Data Reduction & Simplification

This dataset contains 29 620 building footprints with a simple geometry - circular or rectangular. After data alignment, we will remove the buildings from the dataset that overlap with buildings that already exist on OSM.

Tagging Plans

Most of the attributes of the original dataset are irrelevant, only the type of building is considered as relevant because it contains information about the material of the roof. To determine these tags we used the tags as described on the following OSM wiki page: https://wiki.openstreetmap.org/wiki/Key:roof:material

In addition all features will be tagged with building=yes.

Find here a list with all original attributes and their corresponding translation into the OSM tagging schema:

Lingo
Z_GIS attributes Z_GIS attributes meaning OSM tag Comments
objectid=* Id of the building. / Irrelevant
fid_cod_li=* ? / Irrelevant
msfinfo_co=* ? / Irrelevant
orig_oid=* ? / Irrelevant
area_sqm=* Size of the building in squared meters. / Irrelevant
type=
  • very_large_metal
  • large_metal
  • medium_metal
  • small_metal
  • very_small_metal
  • organic
  • plastic
  • facility
  • other_building
Information about the type of the building. roof:material=
  • roof:material=metal
  • roof:material=metal
  • roof:material=metal
  • roof:material=metal
  • roof:material=metal
  • roof:material=palm_leaves
  • roof:material=plastic
  • /
  • /
There will be no tags given to the original attributes type=facility and type=other_building, since the material of the roof is unknown for these features.
sensor=WV3 Satellite that took the imagery that was used to retrieve the buildings. / This will not be added as a tag in the data, but will be added as changeset comment.
country=COD ISO3 country code of Democratic Republic of Congo, the country where the buildings are. / Irrelevant
location=Lingo Name of the area where the buildings are. / Irrelevant
req_code=2020_05 ? / Irrelevant
acq_date=2020-01-01 Date satellite imagery was acquired. / This will not be added as a tag in the data, but will be added as changeset comment.
st_area_sh=* Automatically calculated value - not sure what it is exactly / Irrelevant
st_lenght_=* Automatically calculated value - not sure what it is exactly / Irrelevant
st_area__1=* Automatically calculated value - not sure what it is exactly / Irrelevant
st_perimet=* Automatically calculated value - not sure what it is exactly / Irrelevant
Lopa
Z_GIS attributes Z_GIS attributes meaning OSM tag Comments
FID_COD_Lo=* ? / Irrelevant
id=0 Id of the building. / Irrelevant - there is no data in the original files
orig_oid=* ? / Irrelevant
status=0 Status of the building. / Irrelevant - there is no data in the original files
area_sqm Size of the building in squared meters. / Irrelevant
type=
  • large_sized_metal
  • medium_sized_metal
  • small_sized_metal
  • very_small_sized_metal
  • organic
  • plastic
  • facility
  • other_building
Information about the type of the building. roof:material=
  • roof:material=metal
  • roof:material=metal
  • roof:material=metal
  • roof:material=metal
  • roof:material=palm_leaves
  • roof:material=plastic
  • /
  • /
There will be no tags given to the original attributes type=facility and type=other_building, since the material of the roof is unknown for these features.
sensor=WV2 Satellite that took the imagery that was used to retrieve the buildings. / This will not be added as a tag in the data, but will be added as changeset comment.
county=COD ISO3 country code of Democratic Republic of Congo, the country where the buildings are. / Irrelevant
req_code=2020_06 ? / Irrelevant
location=Lopa Name of the area where the buildings are. / Irrelevant
st_area_sh Automatically calculated value - not sure what it is exactly / Irrelevant
st_lenght_ Automatically calculated value - not sure what it is exactly / Irrelevant


Changeset Tags

We plan to use the following changeset tags

  • comment=#LopaLingobuildingimport #Ituri #RDCongo #Z_GIS #MSF #MissingMaps
  • source=WV3 (for Lingo) OR source=WV2 (for Lopa)
  • source:date=2020-01-01 (for the Lingo dataset) OR source:date=2020-15-01 (for the Lopa dataset)

Data Transformation

The datasets are created in a different projection as the OSM database and based on a different image than currently is used for mapping on OSM. This means that there is a slight misalignment between OSM and the datasets that we will have to correct.

Currently there are 559 buildings from the MSF dataset that overlap with highways on OSM. We will re-align the MSF dataset to fix this issue. If there are after this process still errors, they will be corrected manually.

Data Transformation Results

The data transformation will be done in ArcGIS/Qgis and the results can be found here. The link is temporary and will be removed once the import is finished.

Data Merge Workflow

Team Approach

The import will be undertaken by MSF staff and experienced OSM mappers from the Missing Maps volunteer community, using a specific import OSM user account: https://www.openstreetmap.org/user/MSF_imports.

References

This import is referenced in the Import Catalogue. The local OSM community will be contacted and asked for input on the import plans.

Workflow

The upload will be done in chunks through JOSM. We will follow the next steps:

  1. Data preparation in ArcGIS/Qgis:
    • Align the data with existing data on OSM.
    • Unnecessary attributes are deleted and converted to fit the OSM tagging scheme
    • Buildings that are after the alignment still overlapping with buildings on OSM are removed from the dataset.
    • Split the shapefile in chunks of 500 buildings
    • save the shapefiles as .osm files
  2. Open the .osm file into JOSM (open data plugin needs to be installed)
  3. Download OSM data in the same layer
  4. Validate through the JOSM validation tool, correct remaining errors manually (most likely this will be buildings overlapping with roads).
  5. The validated data is uploaded to OSM with the above described account and changeset tags.

Conflation

As there is not much data in OSM in the area, this does not cause a lot of problems. Without any data transformation yet only 284 buildings overlap with existing OSM buildings. So we will simply remove all buildings from our dataset that are overlapping with buildings already in OSM.

See also

The email to the Imports mailing list was sent on YYYY-MM-DD and can be found in the archives of the mailing list at [1].