Closed msbarry closed 5 months ago
Base 328e1b4d536dd7da38a192e56f4014a18c23a63b | This Branch 2b1ff27ae29e9448ac55a1f9cfee473392fe2562 |
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``` 0:01:00 DEB [archive] - Tile stats: 0:01:00 DEB [archive] - Biggest tiles (gzipped) 1. 14/4942/6092 (153k) https://onthegomap.github.io/planetiler-demo/#14.5/41.82864/-71.40015 (poi:81k) 2. 9/154/190 (148k) https://onthegomap.github.io/planetiler-demo/#9.5/41.77078/-71.36719 (landcover:85k) 3. 10/308/380 (137k) https://onthegomap.github.io/planetiler-demo/#10.5/41.90214/-71.54297 (landcover:66k) 4. 10/308/381 (136k) https://onthegomap.github.io/planetiler-demo/#10.5/41.63994/-71.54297 (landcover:72k) 5. 14/4941/6092 (111k) https://onthegomap.github.io/planetiler-demo/#14.5/41.82864/-71.42212 (poi:63k) 6. 14/4941/6093 (110k) https://onthegomap.github.io/planetiler-demo/#14.5/41.81227/-71.42212 (building:62k) 7. 14/4940/6092 (99k) https://onthegomap.github.io/planetiler-demo/#14.5/41.82864/-71.44409 (building:92k) 8. 11/616/762 (99k) https://onthegomap.github.io/planetiler-demo/#11.5/41.7057/-71.63086 (landcover:71k) 9. 11/616/761 (96k) https://onthegomap.github.io/planetiler-demo/#11.5/41.83679/-71.63086 (landcover:72k) 10. 14/4942/6091 (95k) https://onthegomap.github.io/planetiler-demo/#14.5/41.84501/-71.40015 (building:79k) 0:01:00 DEB [archive] - Max tile sizes z0 z1 z2 z3 z4 z5 z6 z7 z8 z9 z10 z11 z12 z13 z14 all boundary 154 374 443 583 933 334 428 543 543 1.6k 2.1k 7.2k 6.4k 5.8k 4.5k 7.2k water 7.7k 3.7k 8.6k 5.5k 2.6k 5.1k 15k 18k 16k 25k 15k 13k 17k 15k 12k 25k place 0 0 441 441 441 650 739 1k 1.7k 3.4k 5.9k 3.3k 1.7k 797 947 5.9k landuse 0 0 0 0 548 619 1.3k 5.9k 17k 44k 59k 50k 38k 19k 12k 59k transportation 0 0 0 0 464 915 1.2k 6k 8k 24k 17k 19k 64k 47k 33k 64k waterway 0 0 0 0 111 118 0 0 0 3.2k 2.1k 2.1k 2.1k 4.9k 2.4k 4.9k park 0 0 0 0 0 0 1k 3.8k 9.7k 18k 13k 7.7k 4.3k 3.4k 4.4k 18k transportation_name 0 0 0 0 0 0 359 454 1.2k 1.7k 5.4k 4.6k 3.8k 3.4k 18k 18k landcover 0 0 0 0 0 0 0 9.5k 29k 85k 72k 81k 53k 30k 24k 85k mountain_peak 0 0 0 0 0 0 0 1.1k 1.8k 3.4k 4.3k 2.8k 1.4k 1.4k 869 4.3k water_name 0 0 0 0 0 0 0 0 0 486 461 433 452 1.2k 1.5k 1.5k aerodrome_label 0 0 0 0 0 0 0 0 0 0 674 327 273 220 220 674 aeroway 0 0 0 0 0 0 0 0 0 0 1.6k 2.1k 3k 3.4k 2.7k 3.4k poi 0 0 0 0 0 0 0 0 0 0 0 0 501 498 81k 81k building 0 0 0 0 0 0 0 0 0 0 0 0 0 59k 92k 92k housenumber 0 0 0 0 0 0 0 0 0 0 0 0 0 0 35k 35k full tile 7.9k 4k 9.5k 6.5k 3.8k 6.1k 20k 41k 84k 203k 184k 135k 113k 127k 242k 242k gzipped 6.2k 3.5k 7.1k 5.2k 3.1k 4.9k 13k 29k 60k 148k 137k 99k 83k 91k 153k 153k 0:01:00 DEB [archive] - Max tile: 242k (gzipped: 153k) 0:01:00 DEB [archive] - Avg tile: 5.4k (gzipped: 4k) using weighted average based on OSM traffic 0:01:00 DEB [archive] - # tiles: 4,115,012 0:01:00 DEB [archive] - # features: 5,470,173 0:01:00 INF [archive] - Finished in 18s cpu:1m4s avg:3.6 0:01:00 INF [archive] - read 1x(3% 0.5s wait:16s) 0:01:00 INF [archive] - encode 4x(55% 10s wait:2s) 0:01:00 INF [archive] - write 1x(22% 4s wait:12s) 0:01:00 INF - Finished in 1m cpu:3m14s gc:1s avg:3.2 0:01:00 INF - FINISHED! 0:01:00 INF - 0:01:00 INF - ---------------------------------------- 0:01:00 INF - data errors: 0:01:00 INF - render_snap_fix_input 16,628 0:01:00 INF - osm_multipolygon_missing_way 74 0:01:00 INF - osm_boundary_missing_way 70 0:01:00 INF - merge_snap_fix_input 14 0:01:00 INF - feature_centroid_if_convex_osm_invalid_multipolygon_empty_after_fix 2 0:01:00 INF - feature_polygon_osm_invalid_multipolygon_empty_after_fix 2 0:01:00 INF - omt_park_area_osm_invalid_multipolygon_empty_after_fix 1 0:01:00 INF - ---------------------------------------- 0:01:00 INF - overall 1m cpu:3m14s gc:1s avg:3.2 0:01:00 INF - lake_centerlines 2s cpu:5s avg:2.2 0:01:00 INF - read 1x(22% 0.4s done:2s) 0:01:00 INF - process 4x(0% 0s done:2s) 0:01:00 INF - write 1x(0% 0s done:1s) 0:01:00 INF - water_polygons 14s cpu:37s avg:2.7 0:01:00 INF - read 1x(40% 6s done:7s) 0:01:00 INF - process 4x(26% 4s wait:4s done:5s) 0:01:00 INF - write 1x(4% 0.5s wait:9s done:5s) 0:01:00 INF - natural_earth 6s cpu:12s avg:1.8 0:01:00 INF - read 1x(95% 6s) 0:01:00 INF - process 4x(13% 0.8s wait:6s) 0:01:00 INF - write 1x(0% 0s wait:6s) 0:01:00 INF - osm_pass1 2s cpu:6s avg:3.4 0:01:00 INF - read 1x(2% 0s wait:2s) 0:01:00 INF - parse 4x(33% 0.6s) 0:01:00 INF - process 1x(69% 1s) 0:01:00 INF - osm_pass2 17s cpu:1m6s avg:3.9 0:01:00 INF - read 1x(0% 0s wait:9s done:7s) 0:01:00 INF - process 4x(76% 13s) 0:01:00 INF - write 1x(2% 0.4s wait:16s) 0:01:00 INF - boundaries 0s cpu:0s avg:1.7 0:01:00 INF - agg_stop 0s cpu:0s avg:0 0:01:00 INF - sort 1s cpu:3s avg:2.6 0:01:00 INF - worker 1x(50% 0.7s) 0:01:00 INF - archive 18s cpu:1m4s avg:3.6 0:01:00 INF - read 1x(3% 0.5s wait:16s) 0:01:00 INF - encode 4x(55% 10s wait:2s) 0:01:00 INF - write 1x(22% 4s wait:12s) 0:01:00 INF - ---------------------------------------- 0:01:00 INF - archive 107MB 0:01:00 INF - features 280MB -rw-r--r-- 1 runner docker 66M Jan 21 01:39 run.jar ``` | ``` 0:01:04 DEB [archive] - Tile stats: 0:01:04 DEB [archive] - Biggest tiles (gzipped) 1. 14/4942/6092 (153k) https://onthegomap.github.io/planetiler-demo/#14.5/41.82864/-71.40015 (poi:81k) 2. 9/154/190 (148k) https://onthegomap.github.io/planetiler-demo/#9.5/41.77078/-71.36719 (landcover:85k) 3. 10/308/380 (137k) https://onthegomap.github.io/planetiler-demo/#10.5/41.90214/-71.54297 (landcover:66k) 4. 10/308/381 (136k) https://onthegomap.github.io/planetiler-demo/#10.5/41.63994/-71.54297 (landcover:72k) 5. 14/4941/6092 (111k) https://onthegomap.github.io/planetiler-demo/#14.5/41.82864/-71.42212 (poi:63k) 6. 14/4941/6093 (110k) https://onthegomap.github.io/planetiler-demo/#14.5/41.81227/-71.42212 (building:62k) 7. 14/4940/6092 (99k) https://onthegomap.github.io/planetiler-demo/#14.5/41.82864/-71.44409 (building:92k) 8. 11/616/762 (99k) https://onthegomap.github.io/planetiler-demo/#11.5/41.7057/-71.63086 (landcover:71k) 9. 11/616/761 (96k) https://onthegomap.github.io/planetiler-demo/#11.5/41.83679/-71.63086 (landcover:72k) 10. 14/4942/6091 (95k) https://onthegomap.github.io/planetiler-demo/#14.5/41.84501/-71.40015 (building:79k) 0:01:04 DEB [archive] - Max tile sizes z0 z1 z2 z3 z4 z5 z6 z7 z8 z9 z10 z11 z12 z13 z14 all boundary 154 374 443 583 933 334 428 543 543 1.6k 2.1k 7.2k 6.4k 5.8k 4.5k 7.2k water 7.7k 3.7k 8.6k 5.5k 2.6k 5.1k 15k 18k 16k 25k 15k 13k 17k 15k 12k 25k place 0 0 441 441 441 650 739 1k 1.7k 3.4k 5.9k 3.3k 1.7k 797 947 5.9k landuse 0 0 0 0 548 619 1.3k 5.9k 17k 44k 59k 50k 38k 19k 12k 59k transportation 0 0 0 0 464 915 1.2k 6k 8k 24k 17k 19k 64k 47k 33k 64k waterway 0 0 0 0 111 118 0 0 0 3.2k 2.1k 2.1k 2.1k 4.9k 2.4k 4.9k park 0 0 0 0 0 0 1k 3.8k 9.7k 18k 13k 7.7k 4.3k 3.4k 4.4k 18k transportation_name 0 0 0 0 0 0 359 454 1.2k 1.7k 5.4k 4.6k 3.8k 3.4k 18k 18k landcover 0 0 0 0 0 0 0 9.5k 29k 85k 72k 81k 53k 30k 24k 85k mountain_peak 0 0 0 0 0 0 0 1.1k 1.8k 3.4k 4.3k 2.8k 1.4k 1.4k 869 4.3k water_name 0 0 0 0 0 0 0 0 0 486 461 433 452 1.2k 1.5k 1.5k aerodrome_label 0 0 0 0 0 0 0 0 0 0 674 327 273 220 220 674 aeroway 0 0 0 0 0 0 0 0 0 0 1.6k 2.1k 3k 3.4k 2.7k 3.4k poi 0 0 0 0 0 0 0 0 0 0 0 0 501 498 81k 81k building 0 0 0 0 0 0 0 0 0 0 0 0 0 59k 92k 92k housenumber 0 0 0 0 0 0 0 0 0 0 0 0 0 0 35k 35k full tile 7.9k 4k 9.5k 6.5k 3.8k 6.1k 20k 41k 84k 203k 184k 135k 113k 127k 242k 242k gzipped 6.2k 3.5k 7.1k 5.2k 3.1k 4.9k 13k 29k 60k 148k 137k 99k 83k 91k 153k 153k 0:01:04 DEB [archive] - Max tile: 242k (gzipped: 153k) 0:01:04 DEB [archive] - Avg tile: 5.4k (gzipped: 4k) using weighted average based on OSM traffic 0:01:04 DEB [archive] - # tiles: 4,115,012 0:01:04 DEB [archive] - # features: 5,470,173 0:01:04 INF [archive] - Finished in 18s cpu:1m4s avg:3.6 0:01:04 INF [archive] - read 1x(3% 0.5s wait:16s) 0:01:04 INF [archive] - encode 4x(56% 10s wait:2s) 0:01:04 INF [archive] - write 1x(21% 4s wait:12s) 0:01:04 INF - Finished in 1m5s cpu:3m18s avg:3.1 0:01:04 INF - FINISHED! 0:01:04 INF - 0:01:04 INF - ---------------------------------------- 0:01:04 INF - data errors: 0:01:04 INF - render_snap_fix_input 16,628 0:01:04 INF - osm_multipolygon_missing_way 74 0:01:04 INF - osm_boundary_missing_way 70 0:01:04 INF - merge_snap_fix_input 14 0:01:04 INF - feature_centroid_if_convex_osm_invalid_multipolygon_empty_after_fix 2 0:01:04 INF - feature_polygon_osm_invalid_multipolygon_empty_after_fix 2 0:01:04 INF - omt_park_area_osm_invalid_multipolygon_empty_after_fix 1 0:01:04 INF - ---------------------------------------- 0:01:04 INF - overall 1m5s cpu:3m18s avg:3.1 0:01:04 INF - lake_centerlines 3s cpu:5s avg:2.1 0:01:04 INF - read 1x(17% 0.5s done:2s) 0:01:04 INF - process 4x(0% 0s done:2s) 0:01:04 INF - write 1x(0% 0s done:2s) 0:01:04 INF - water_polygons 14s cpu:38s avg:2.7 0:01:04 INF - read 1x(41% 6s done:7s) 0:01:04 INF - process 4x(27% 4s wait:4s done:5s) 0:01:04 INF - write 1x(4% 0.5s wait:9s done:5s) 0:01:04 INF - natural_earth 10s cpu:16s avg:1.6 0:01:04 INF - read 1x(59% 6s done:4s) 0:01:04 INF - process 4x(8% 0.8s wait:6s done:4s) 0:01:04 INF - write 1x(0% 0s wait:6s done:4s) 0:01:04 INF - osm_pass1 2s cpu:6s avg:3.2 0:01:04 INF - read 1x(2% 0s wait:2s) 0:01:04 INF - parse 4x(32% 0.6s) 0:01:04 INF - process 1x(71% 1s) 0:01:04 INF - osm_pass2 16s cpu:1m4s avg:3.9 0:01:04 INF - read 1x(0% 0s wait:9s done:7s) 0:01:04 INF - process 4x(76% 12s) 0:01:04 INF - write 1x(2% 0.4s wait:16s) 0:01:04 INF - boundaries 0s cpu:0s avg:2.5 0:01:04 INF - agg_stop 0s cpu:0s avg:0 0:01:04 INF - sort 1s cpu:3s avg:2.6 0:01:04 INF - worker 1x(53% 0.7s) 0:01:04 INF - archive 18s cpu:1m4s avg:3.6 0:01:04 INF - read 1x(3% 0.5s wait:16s) 0:01:04 INF - encode 4x(56% 10s wait:2s) 0:01:04 INF - write 1x(21% 4s wait:12s) 0:01:04 INF - ---------------------------------------- 0:01:04 INF - archive 107MB 0:01:04 INF - features 280MB -rw-r--r-- 1 runner docker 66M Jan 21 01:38 run.jar ``` |
https://github.com/onthegomap/planetiler/actions/runs/7598162876
Kudos, no new issues were introduced!
0 New issues
0 Security Hotspots
73.5% Coverage on New Code
0.0% Duplication on New Code
Beautiful!
So if I have two layers, one called "landcover" and one called "climate zone", can I take the landcover polygons, intersect them with the climate zone polygons, and then tag the landcover polygons with the climate zone class that makes the biggest overlap?
Another use case I would like to try out for this: in some countries and regions there are not many road, but minzooms are usually done at a global level. So when you set minzooms that are good in Europe, the map might be quite empty in Africa. Could I use this new feature to compute road density in each tile and then drop roads accordingly?
You could definitely do the first one, just need to do some lower-level grouping by tags in java, then JTS operations. If it seems like a common pattern, we could pull it into a reusable utility in planetiler.
For the second one you could emit more roads then you need during process step, then filter them out based on # roads in a tile in postprocess, but I'd be concerned about discontinuities at the tile seams? How were you thinking of dropping based on road density?
Discontinuities will be there at tile boundaries, but I would still like to try it.
Filtering could look like this:
Add a new
Map<String, List<VectorTile.Feature>> postProcessTileFeatures(TileCoord, Map<String, List<VectorTile.Feature>>)
hook that profiles can implement to post-process features across layers. For example:See #786