Basically in Qgis you should save WKT and OSM data in an editable format like Geopackage.
Removing small and low confidence geometries from candidate dataset can be done via table editor “select by expression”.
Removing geometries touching OSM buildings: click menu “processing”, find a search field and type “Select by Location”. Set the layer fields and basically all you need to do is checkbox where elements are disjoined. Run and save resulting layer in geojson for loading in JOSM.
Area: I supposed a 5x5 meters room is the minimum for dwelling and smaller shapes are closer and closer to pixel size, hence prone to errors: do we prefer more huts with higher false positive ratio? Of course the local community contribution is important, particularly on these issues.
Confidence: I took this suggestion that seems to me reasonable; if we had human resources that can check data quality broadly, I would drop this filter.
Anyway I think going for with relatively large and reliable objects could be a starting point: if needed, later you can populate further, following the same procedure.
Looks like you process the WKT. Probably working on Geopackage (or shp) will solve the issue. Personally I extracted from 50k polys with 7k OSM input in 5-6 minutes, using part of 8G RAM.
Saya pakai angka threshold yang dipilih Cascafico sebelumnya. Tujuan utamanya untuk menyaring data berkualitas. Karena ada kemungkinan kalau bangunan sempit (area_in_meters) dengan akurasi yang rendah (confidence) itu hanya artefak hasil pemrosesan AI-nya saja.
Untuk angka threshold pastinya, tidak harus 0.75 dan 25 sih. Mungkin ada kombinasi angka lain yang lebih bagus. Tapi untuk mengetahuinya secara pasti, kita perlu citra satelit yang terbaru / verifikasi langsung kontributor OSM yang ada di lokasi, untuk memverifikasi setiap bangunan yang ada di dataset ini – secara manual.
Looks like you process the WKT. Probably working on Geopackage (or shp) will solve the issue. Personally I extracted from 50k polys with 7k OSM input in 5-6 minutes, using part of 8G RAM.
Thank you for sharing and addressing the issues above.
I loaded it in JOSM, added the tag “building=yes,” and ran the validation, resulting in this output (picture attached). Could you please confirm if this is the expected result and the correct workflow to import?
Your imported buildings looks amazing with complete changeset comment and source.
But I noticed some weird buildings shape like this: 1, 2.
Thanks for you post-import report. For some reason I’ve left out about a hundred geometric warnings. I’m solving in a single changeset, but I don’t understand why they were not raised in JOSM before uploading.
Thank you. This is what I can do voluntarily for your effort. I checked your uploaded data in Sabu Raijua and fixed some self-intersection ways and crossing building.
Sometimes it happened to me as well, JOSM shows nothing before uploading. I noticed new(?) warnings after uploading my data. I usually re-download my uploaded data as a new layer and run the JOSM validator.
I found Google uses the latest imagery as they mentioned on their page, this building still does not exist based on Bing, but it appears in the Google Maps satellite mode. I leave it as is for now. This leaves us with a gap if we use Google’s dataset.
I confirm images used for buildings recognition are pretty fresh, or fresher than Bing. Somewhere in Argentina also. It seems Maxar (which usually is updated in Europe) is still unavailable, so right now in many cases we cannot double check for false positive buildings.