The file roof_types_sxm.csv
contains the data from a manually mapped session. This data has been mapped on aerial imagery in an offline ID-editor setup. Purpose of this script is adding this data to osm.
The data already contains osm id's so spatial joining is not needed.
-
Run
csv_clean_up.py
to remove all unnecessary information (empty rows and unused columns) and cast our values to the ones used by osm. This scripts outputsroof_types_sxm_clean.csv
. -
Download
buildings_sxm_input.osm
using overpass turbo with the following query:
[out:xml][timeout:25];
// gather results
(
// query part for: “building=*”
node["building"](17.99979487484851,-63.15679550170898,18.126112640728326,-62.99491882324219);
way["building"](17.99979487484851,-63.15679550170898,18.126112640728326,-62.99491882324219);
relation["building"](17.99979487484851,-63.15679550170898,18.126112640728326,-62.99491882324219);
);
(._;>;);
out meta;
-
Run
add_rooftypes.py
which will add the roof shapes and materials to the osm objects, only if they do not exist in osm yet. The output is calledbuildings_sxm_output.osm
. -
Upload the data from
buildings_sxm_output.osm
by using JOSM.