SAU data to OSM/Data transformation and follow up

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Data overview

The following table presents data tables and transformation we will need to apply to them.
Date from satellite/aerial imagery will be calculated for all fields concerned.

Layer name Contents Quality check Import order Quality improvment Selection on data Data preparation process
edi_bati buildings and usage when available intermediate 2 SAU to review qualification/classification and to remove buildings that don't exist anymore. Cut building with the limit roof from linear limits layer.
edi_construction_p punctual manmade points (beacon, antenna, power pole...) ok 4
edi_construction_l linear manmade limits (fence, wall, dam...) ok 6
edi_construction_s horizontal man made objects (such as parking, swimming pool, etc). intermediate 7 SAU to review qualification/classification and check marae that are (or not) open to public Select swimming pool open to public
voi_mobilier_route_p highway furniture (KM point, lamp..) ok 4 KM point, crossing, traffic calming to be projected on the road.
voi_troncon_route_l highway intermediate 1 SAU to review qualification/classification
rel_lignes_repere contour lines, and specific contour lines (cliff...) intermediate 6 SAU to review qualification/classification selection on cliffs only
rel_points_cotes figure field including peaks and other remarkable points yes 4 selection on remarkable points (peaks, etc).
ref_repere geodesic reference point ok 5 selection on SAU RGPF points with a code_repere compilation of fields and extraction of elevation in a specific field (original layer is 2.5 pt layer).
nom_pai point of interest intermediate 3 SAU to review qualification/classification
nom_toponyme localty names ok 4

Data transformation tables and process

Color code  :

Ready for transformation Being improved by SAU

Common

Process:

  • extract date of imagery when acquisition mode is imagery (satellite or aerial photogrammetry).

Construction

Buildings

Data quality : We estimated 5 to 10% the percentage of update lack (buildings existed when they where mapped but have been destroyed and replaced by something else such as parking now) on SAU building layers. So SAU is cleaning up this layer removing buildings that don't exist anymore. The one that were not mapped won't be added at this stage as it represents a high need of work and mean (SAU buildings are 2.5D and come from stereopreparation J). Which will lead to import of buildings that do exist. On top of that SAU is correcting classification incoherence. So we suppose that buildings imported will be of high quality and will be of a high benefit for OSM. To be noted that there is a big lack of buildings in OSM in French Polynesia, and mainly in Tahiti. SAU corrected buildings for Society islands.

Process :

  • Cut building with the limit roof from linear limits layer
  • Extract centroid from transformers

Quality improvement : Update types from name when available

Manmade points

Process: add centroids of transformers from building layer

Linear limits

Horizontal man made area

Data quality : SAU to clean up classification

Quality improvement : update types from names when available

Selection: Swimming pools with no names excluded as most of them are private.

Highway

Furnitures

Process: categories 0, 3, 4 to be projected on the highway as they need to be on a point of the highway.

Highways

Data quality : SAU is cleaning and improving this dataset (geometry and qualification). Finally, following the trial on Arue pilote, this is more part of an integration process as everything has gone manually, following those reasons :

  • difficulty to automatically cut or expand when needed (need manual interaction)
  • difficulty to find road equivalent from one dataset to the other due to itinerary and itinerary break differences
  • difficulty to merge one road network to OSM road network as OSM may have attributes on points (need manual interaction)
  • easier to go all manual due to the quantity of data to integer

Topography

Cliffs and ridge

Data quality : SAU is cleaning cliffs orientation.

Selection : select cliffs and ridge only from contour line layer.

Remarkable points

Selection : only remarkable topographic points (peaks, saddles)

Process: extract elevation in a specific field (original layer is 2.5 pt layer).

ele:local is used to say that the altimetric reference system has been built with GPS altimetry. This is not proper precise levelling process and this way is independant from NGPF (general levelling for french polynesia)

Geodesic reference points

Selection : from RGPF only, with a code_repere

Process :

  • adapt ele ref system, name depending on location.
  • Get lat, long, ele from geometry

Info: id_repere is the unique id of the reference point, code_repere is the name used for it. id_repere is unique, code_repere is not unique for the whole of FP. code_repere + island ID is unique. So this is the concatenation being used for name.

SAU is going to publish the geodesic reference points pdf records on the future, which will be possible to gather through id_repere (ref) and source = SAU in the future. So url and url:description are temporary and could be removed from this time and replaced with Tag2Link use.

Point of interest

Data quality : classification incoherence being cleaned by SAU.

Quality Improvement : classify from name automatically

Localities

Import follow up

Layer name Contents Pilot Changeset List of all changesets
edi_bati buildings and usage when available #74988674, #74988677, #74988683, #74988693 https://www.openstreetmap.org/user/A%20Vos%20Cartes%20-%20Imports/history#map=5/-16.380/-144.800
edi_construction_p punctual manmade points (beacon, antenna, power pole...) #75027116
edi_construction_l linear manmade limits (fence, wall, dam...) #75026237
edi_construction_s horizontal man made objects (such as parking, swimming pool, etc). #75026934
voi_mobilier_route_p highway furniture (KM point, lamp..) #75030231
voi_troncon_route_l highway #74936826
rel_lignes_repere contour lines, and specific contour lines (cliff...) #75029646
rel_points_cotes figure field including peaks and other remarkable points nodata
ref_repere geodesic reference point #75029614
nom_pai point of interest #75028891
nom_toponyme localty names #75029107