---
license: odbl
task_categories:
  - other
language:
  - en
tags:
  - geospatial
  - openstreetmap
  - osm
  - polygons
  - landuse
  - landcover
  - remote-sensing
  - foundation-model
size_categories:
  - 1M<n<10M
---

# osm-polygon-selection dataset

A curated set of OpenStreetMap polygons from **310
geographic units** — *sovereign countries plus sub-country regions*
like Brazilian states, Chinese provinces, Indian zones, US states,
Canadian provinces, Japanese regions, and Indonesian islands —
classified by **size bin** (`small` / `medium` / `large`, area in
[0.1, 100] km²) and tagged by continent (Natural Earth admin0 lookup).

**Size bins:**

- **`small`** — area in **[0.1, 1) km²** (10,000 m² to 1 km², roughly
  100 m × 100 m to ~1 km × 1 km). Examples: a city block, a small
  park, a single farm field, a small wood lot, a residential
  courtyard, a parking lot, an industrial yard.
- **`medium`** — area in **[1, 10) km²** (1 km² to 10 km², roughly
  1 km × 1 km to 3 km × 3 km). Examples: a large park, a small
  village/town footprint, a reservoir, a forest patch, an
  industrial zone, a golf course, a cemetery, a nature reserve.
- **`large`** — area in **[10, 100] km²** (10 km² to 100 km², roughly
  3 km × 3 km to 10 km × 10 km). Examples: a large forest, a
  big lake, an entire town or small city, a large military
  training area, a national park section, a sizable agricultural
  region.

Polygons smaller than 0.1 km² (most individual buildings, houses,
small ponds, single fields) and larger than 100 km² (whole countries,
mountain ranges, big seas) are excluded by the size filter (see
[Filter chain](#filter-chain) below).

**Status:** All 310 geographic units are extracted end-to-end.

**Total polygons:** 16,297,690
(combined parquet: `combined/all_world.parquet`).

## Coverage

This dataset processes one parquet per Geofabrik PBF region. Each
region is bucketed in `per_country/` as either a **sovereign
country** or a **sub-country region** (state, province, federal
district, island group, zone):

| unit type | count | examples |
|-----------|------:|----------|
| Sovereign country | ~170 | france, ghana, japan, peru, australia, brazil, argentina |
| Sub-country region | ~130 | brazil-sudeste, china-beijing, japan-kanto, india-central-zone, us-texas, canada-ontario, russia-siberian-fed-district |
| Multi-country bundle | ~10 | gcc-states, ireland-and-northern-ireland, senegal-and-gambia, haiti-and-domrep, malaysia-singapore-brunei, israel-and-palestine |

Total: **310 geographic units** across all 6 inhabited
continents plus Oceania. The **310** is **not** the count
of sovereign states (which is ~195 per the UN); it's the count of
discrete Geofabrik PBF regions we processed.

## Layout

This dataset is split across five subfolders so you can pull only what
you need:

| folder | what's inside | typical size |
|--------|---------------|--------------|
| [`per_country/`](./per_country/) | one folder per country with `<country>.parquet` + `README.md` | ~28 GB total, <1 MB per small country |
| [`combined/`](./combined/) | `all_world.parquet` — every polygon in one file | ~14 GB |
| [`splits/`](./splits/) | `train.parquet`, `val.parquet`, `test.parquet` — pre-filtered split parquets (no `split` column needed) | ~13 GB total |
| [`sample/`](./sample/) | `sample_map.jsonl` — ~18k representative polygons for quick viz | ~3 MB |
| [`preview/`](./preview/) | `map_preview.png` — static map thumbnail | ~1 MB |

Start with `sample/` or `preview/` for a quick look. Pull
`per_country/<country>/<country>.parquet` for a single-country
study. Use `combined/all_world.parquet` for cross-country work
or `splits/<train/val/test>.parquet` for ML training with the
pre-defined 80/10/10 stratified-by-country split (seed=42).

## What's in this dataset

Each row is one OSM polygon (closed way or multipolygon relation) that
passed our filter chain (see below). The polygon **geometry itself**
is included in the row as WKT (or WKB if `OSM_POLYGON_GEOMETRY=wkb`
is set when the dataset is built) so you can render, query, or
reproject it directly without re-deriving from centroid+area.

| column | type | description |
|--------|------|-------------|
| osm_id | int64 | OSM object id (int64). |
| osm_type | string | OSM object type, "way" or "relation" (string). |
| centroid_lon | float64 | polygon centroid longitude (WGS84, float64). |
| centroid_lat | float64 | polygon centroid latitude (WGS84, float64). |
| area_km2 | float64 | polygon area in km² (Web Mercator, float64). |
| tags | list(string) | OSM `key=value` tags (list of strings). |
| matched_tag | string | the first tag in `tags` that hit the whitelist (string, the reason the polygon survived). |
| continent | string | Natural Earth admin0 lookup of the centroid (string). |
| size_bin | string | "small" (0.1-1), "medium" (1-10), or "large" (10-100) km² (string). |
| country | string | ISO-style country name (string). |
| extract_status | string | "clean" (extract ran to completion) or "killed" (extract was interrupted) (string). |
| pbf_date | string | date of the source PBF file (string, from mtime). |
| geometry_wkt | string | polygon geometry as WKT (WGS84, string). Parse with `shapely.wkt.loads(row.geometry_wkt)`. |

## Provenance

- Pipeline version: v0.1.0
- Git SHA: d69b105c41732c72c2162d737e3353b95bcbdfbf
- Built: 2026-07-06T23:15:51.406227
- Source: Geofabrik regional extracts (`https://download.geofabrik.de/`)
- Whitelist: 22,075 OSM `key=value` tags from osm-stats (see
  `docs/whitelist_decisions.md` in the project repo, or read the
  full rationale in the
  [blog post](https://noeflandre.com/posts/osm-data-analysis)).
  The whitelist is designed to filter polygons by landuse-style
  tags (`natural`, `landuse`, `leisure`, `amenity`, etc.) so the
  dataset focuses on physical land-cover / land-use features
  rather than buildings, addresses, or points of interest.

## Geographic distribution

![polygon distribution across the dataset's countries](./preview/map_preview.png)

(Each circle is one polygon from the `sample/` folder, color-coded by
country. Circle size is proportional to `sqrt(area_km2)`.)

## Size-bin distribution (full dataset)

Counts every polygon in the **16,297,690-polygon** dataset
by `size_bin`, computed directly from `combined/all_world.parquet`
via `pyarrow.compute.value_counts`. Percentages are exact ratios
over the entire dataset, not a sample.

| size_bin | count | pct |
|----------|-------|-----|
| small | 12,474,300 | 76.5% |
| medium | 3,294,828 | 20.2% |
| large | 528,562 | 3.2% |
| **Total** | 16,297,690 | 100.0% |


## Example row

Here is one concrete row from the Liechtenstein parquet file
(a `natural=*` polygon, fully filled-in with all 13 columns):

| column | value |
|--------|-------|
| osm_id | `1342399548` |
| osm_type | `way` |
| centroid_lon | 9.513440 |
| centroid_lat | 47.070405 |
| area_km2 | 0.3202 |
| tags | `natural=grassland` |
| matched_tag | `natural=grassland` |
| continent | `Europe` |
| size_bin | `small` |
| country | `liechtenstein` |
| extract_status | `clean` |
| pbf_date | `2026-06-26` |
| geometry_wkt | `MULTIPOLYGON (((9.5109116 47.0686582, 9.5111015 47.0686316, 9.5112088 47.0685293, 9.5112785 47.06...` |


This row is representative: the full-dataset distribution above
shows ~80% `small`, ~18% `medium`, ~2% `large`, and the dominant
whitelist tag families (`natural=*`, `landuse=*`, `leisure=*`)
account for the majority of `matched_tag` values.

## Filter chain

Each polygon in this dataset has passed three filters:

1. **Size filter (Stage 0)**: area in [0.1, 100] km².
   Polygons smaller than 0.1 km² or larger than 100 km² are dropped.
2. **Whitelist filter (Stage 2)**: at least one OSM tag in the
   22,075-tag whitelist. The whitelist is derived from a clustering
   of OSM tags across both `tfidf` and `embeddings` analyses.
3. **Classify (Stage 3)**: continent assigned via Natural Earth
   admin0 shapefile, size_bin assigned by area.

## Train / val / test split

Every row in every parquet (`per_country/<country>/<country>.parquet`
and `combined/all_world.parquet`) carries a **`split`** column with
one of three values: `train`, `val`, or `test`.

| split | ratio | polygons |
|-------|-------|----------|
| train | 80% | 13,037,271 |
| val   | 10% | 1,628,432 |
| test  | 10% | 1,631,987 |
| **Total** | **100%** | **16,297,690** |

The split is **stratified by country**: each country's rows are
assigned to train/val/test independently using a global
`numpy.random.default_rng` seeded once with **42** and
offset per country. The exact counts per country and the seed are
recorded in [`splits/split_manifest.json`](./splits/split_manifest.json).

To load only one split (e.g. for training), filter in pyarrow:
```python
import pyarrow.compute as pc
import pyarrow.parquet as pq
table = pq.read_table("combined/all_world.parquet")
train = table.filter(pc.equal(table["split"], "train"))
```

The split is deterministic and re-runnable:
```bash
uv run python scripts/make_split.py            # default seed=42, 80/10/10
uv run python scripts/make_split.py --seed 7   # different reproducible split
```

## Per-country summary

| Country | Polygons | Status |
|---------|----------|--------|
| afghanistan | 15,821 | clean |
| albania | 14,738 | clean |
| algeria | 32,601 | clean |
| american-oceania | 627 | clean |
| andorra | 776 | clean |
| angola | 19,197 | clean |
| argentina | 193,648 | clean |
| armenia | 7,720 | clean |
| australia | 115,145 | clean |
| austria | 133,711 | clean |
| azerbaijan | 15,826 | clean |
| azores | 2,640 | clean |
| bahamas | 2,882 | clean |
| bangladesh | 11,444 | clean |
| belarus | 223,750 | clean |
| belgium | 125,108 | clean |
| belize | 12,972 | clean |
| benin | 4,614 | clean |
| bhutan | 8,769 | clean |
| bolivia | 30,701 | clean |
| bosnia-herzegovina | 49,715 | clean |
| botswana | 5,327 | clean |
| brazil-centro-oeste | 58,137 | clean |
| brazil-nordeste | 46,565 | clean |
| brazil-norte | 46,154 | clean |
| brazil-sudeste | 59,249 | clean |
| brazil-sul | 65,807 | clean |
| bulgaria | 74,567 | clean |
| burkina-faso | 8,835 | clean |
| burundi | 4,659 | clean |
| cambodia | 4,374 | clean |
| cameroon | 71,789 | clean |
| canada-alberta | 134,835 | clean |
| canada-british-columbia | 148,372 | clean |
| canada-manitoba | 104,499 | clean |
| canada-new-brunswick | 16,313 | clean |
| canada-newfoundland-and-labrador | 91,841 | clean |
| canada-northwest-territories | 246,014 | clean |
| canada-nova-scotia | 23,043 | clean |
| canada-ontario | 182,052 | clean |
| canada-prince-edward-island | 4,579 | clean |
| canada-quebec | 251,374 | clean |
| canada-saskatchewan | 57,366 | clean |
| canada-yukon | 34,726 | clean |
| canary-islands | 2,560 | clean |
| cape-verde | 2,417 | clean |
| central-african-republic | 53,491 | clean |
| chad | 23,667 | clean |
| chile | 71,739 | clean |
| china-anhui | 17,945 | clean |
| china-beijing | 15,710 | clean |
| china-chongqing | 6,062 | clean |
| china-fujian | 13,047 | clean |
| china-gansu | 31,162 | clean |
| china-guangdong | 27,368 | clean |
| china-guangxi | 13,033 | clean |
| china-guizhou | 8,079 | clean |
| china-hainan | 4,158 | clean |
| china-hebei | 63,656 | clean |
| china-heilongjiang | 37,555 | clean |
| china-henan | 18,109 | clean |
| china-hong-kong | 1,519 | clean |
| china-hubei | 16,705 | clean |
| china-hunan | 13,182 | clean |
| china-inner-mongolia | 28,927 | clean |
| china-jiangsu | 29,605 | clean |
| china-jiangxi | 15,265 | clean |
| china-jilin | 16,150 | clean |
| china-liaoning | 12,329 | clean |
| china-macau | 90 | clean |
| china-ningxia | 6,196 | clean |
| china-qinghai | 7,691 | clean |
| china-shaanxi | 28,916 | clean |
| china-shandong | 49,948 | clean |
| china-shanghai | 5,564 | clean |
| china-shanxi | 14,141 | clean |
| china-sichuan | 22,246 | clean |
| china-tianjin | 8,836 | clean |
| china-tibet | 24,681 | clean |
| china-xinjiang | 34,254 | clean |
| china-yunnan | 16,805 | clean |
| china-zhejiang | 30,185 | clean |
| colombia | 36,412 | clean |
| comores | 395 | clean |
| congo-brazzaville | 6,643 | clean |
| congo-democratic-republic | 85,106 | clean |
| costa-rica | 7,476 | clean |
| croatia | 47,140 | clean |
| cuba | 23,386 | clean |
| cyprus | 4,846 | clean |
| czech-republic | 271,062 | clean |
| denmark | 175,795 | clean |
| djibouti | 576 | clean |
| east-timor | 1,553 | clean |
| ecuador | 16,139 | clean |
| egypt | 24,623 | clean |
| el-salvador | 3,517 | clean |
| equatorial-guinea | 1,004 | clean |
| eritrea | 3,278 | clean |
| estonia | 47,160 | clean |
| ethiopia | 29,663 | clean |
| faroe-islands | 1,278 | clean |
| fiji | 3,093 | clean |
| finland | 427,870 | clean |
| france | 492,538 | clean |
| gabon | 3,843 | clean |
| gcc-states | 59,856 | clean |
| georgia | 21,198 | clean |
| germany | 1,131,888 | clean |
| ghana | 11,445 | clean |
| greece | 45,142 | clean |
| greenland | 15,132 | clean |
| guernsey-jersey | 670 | clean |
| guinea | 12,311 | clean |
| guinea-bissau | 2,109 | clean |
| guyana | 2,192 | clean |
| haiti-and-domrep | 4,604 | clean |
| honduras | 6,160 | clean |
| hungary | 77,569 | clean |
| iceland | 47,896 | clean |
| india-central-zone | 51,347 | clean |
| india-eastern-zone | 21,480 | clean |
| india-north-eastern-zone | 12,160 | clean |
| india-northern-zone | 50,862 | clean |
| india-southern-zone | 66,323 | clean |
| india-western-zone | 39,173 | clean |
| indonesia-java | 10,582 | clean |
| indonesia-kalimantan | 7,663 | clean |
| indonesia-maluku | 3,800 | clean |
| indonesia-nusa-tenggara | 7,778 | clean |
| indonesia-papua | 4,208 | clean |
| indonesia-sulawesi | 5,805 | clean |
| indonesia-sumatra | 10,121 | clean |
| iran | 57,028 | clean |
| iraq | 26,861 | clean |
| ireland-and-northern-ireland | 159,879 | clean |
| isle-of-man | 2,648 | clean |
| israel-and-palestine | 13,681 | clean |
| italy | 276,991 | clean |
| ivory-coast | 14,273 | clean |
| jamaica | 1,865 | clean |
| japan-chubu | 14,376 | clean |
| japan-chugoku | 10,128 | clean |
| japan-hokkaido | 18,638 | clean |
| japan-kansai | 9,902 | clean |
| japan-kanto | 16,024 | clean |
| japan-kyushu | 23,846 | clean |
| japan-shikoku | 4,686 | clean |
| japan-tohoku | 18,838 | clean |
| jordan | 4,078 | clean |
| kazakhstan | 78,493 | clean |
| kenya | 16,916 | clean |
| kiribati | 584 | clean |
| kosovo | 5,377 | clean |
| kyrgyzstan | 17,110 | clean |
| laos | 6,380 | clean |
| latvia | 47,133 | clean |
| lebanon | 4,289 | clean |
| lesotho | 19,246 | clean |
| liberia | 2,342 | clean |
| libya | 13,046 | clean |
| liechtenstein | 565 | clean |
| lithuania | 76,550 | clean |
| luxembourg | 11,460 | clean |
| macedonia | 9,330 | clean |
| madagascar | 22,664 | clean |
| malawi | 5,337 | clean |
| malaysia-singapore-brunei | 20,439 | clean |
| maldives | 2,358 | clean |
| mali | 40,392 | clean |
| malta | 620 | clean |
| marshall-islands | 628 | clean |
| mauritania | 9,040 | clean |
| mauritius | 1,863 | clean |
| mayotte | 606 | clean |
| mexico | 68,967 | clean |
| micronesia | 755 | clean |
| moldova | 35,690 | clean |
| monaco | 2 | clean |
| mongolia | 10,941 | clean |
| montenegro | 11,785 | clean |
| morocco | 42,623 | clean |
| mozambique | 11,101 | clean |
| myanmar | 32,570 | clean |
| namibia | 9,316 | clean |
| nepal | 68,869 | clean |
| netherlands | 207,459 | clean |
| new-caledonia | 2,141 | clean |
| new-zealand | 127,834 | clean |
| nicaragua | 10,633 | clean |
| niger | 14,606 | clean |
| nigeria | 33,059 | clean |
| north-korea | 19,295 | clean |
| norway | 413,801 | clean |
| pakistan | 36,200 | clean |
| panama | 6,663 | clean |
| papua-new-guinea | 9,006 | clean |
| paraguay | 29,619 | clean |
| peru | 26,038 | clean |
| philippines | 36,555 | clean |
| poland | 637,908 | clean |
| polynesie-francaise | 2,195 | clean |
| portugal | 66,287 | clean |
| romania | 115,401 | clean |
| russia-central-fed-district | 315,956 | clean |
| russia-crimean-fed-district | 20,645 | clean |
| russia-far-eastern-fed-district | 192,337 | clean |
| russia-kaliningrad | 12,432 | clean |
| russia-north-caucasus-fed-district | 52,352 | clean |
| russia-northwestern-fed-district | 381,413 | clean |
| russia-siberian-fed-district | 291,830 | clean |
| russia-south-fed-district | 166,644 | clean |
| russia-ural-fed-district | 184,300 | clean |
| russia-volga-fed-district | 237,169 | clean |
| rwanda | 3,976 | clean |
| saint-helena-ascension-and-tristan-da-cunha | 174 | clean |
| samoa | 369 | clean |
| sao-tome-and-principe | 190 | clean |
| senegal-and-gambia | 20,479 | clean |
| serbia | 47,189 | clean |
| seychelles | 264 | clean |
| sierra-leone | 3,366 | clean |
| slovakia | 54,888 | clean |
| slovenia | 41,526 | clean |
| solomon-islands | 4,575 | clean |
| somalia | 48,453 | clean |
| south-africa | 120,252 | clean |
| south-korea | 39,039 | clean |
| south-sudan | 17,091 | clean |
| spain | 240,230 | clean |
| sri-lanka | 13,748 | clean |
| sudan | 38,043 | clean |
| suriname | 9,410 | clean |
| swaziland | 4,113 | clean |
| sweden | 397,661 | clean |
| switzerland | 61,156 | clean |
| syria | 21,603 | clean |
| taiwan | 14,124 | clean |
| tajikistan | 24,831 | clean |
| tanzania | 16,046 | clean |
| thailand | 32,789 | clean |
| togo | 3,408 | clean |
| tonga | 916 | clean |
| tunisia | 8,498 | clean |
| turkey | 113,609 | clean |
| turkmenistan | 6,353 | clean |
| uganda | 10,449 | clean |
| ukraine | 645,578 | clean |
| united-kingdom | 205,002 | clean |
| uruguay | 10,051 | clean |
| us-alabama | 28,004 | clean |
| us-alaska | 62,521 | clean |
| us-arizona | 38,727 | clean |
| us-arkansas | 18,513 | clean |
| us-california | 51,998 | clean |
| us-colorado | 61,550 | clean |
| us-connecticut | 12,802 | clean |
| us-delaware | 7,255 | clean |
| us-district-of-columbia | 564 | clean |
| us-florida | 229,464 | clean |
| us-georgia | 29,945 | clean |
| us-hawaii | 2,619 | clean |
| us-idaho | 17,217 | clean |
| us-illinois | 79,460 | clean |
| us-indiana | 42,034 | clean |
| us-iowa | 30,362 | clean |
| us-kansas | 56,789 | clean |
| us-kentucky | 12,179 | clean |
| us-louisiana | 17,618 | clean |
| us-maine | 17,529 | clean |
| us-maryland | 24,684 | clean |
| us-massachusetts | 22,134 | clean |
| us-michigan | 73,209 | clean |
| us-minnesota | 72,441 | clean |
| us-mississippi | 7,704 | clean |
| us-missouri | 48,147 | clean |
| us-montana | 18,606 | clean |
| us-nebraska | 31,375 | clean |
| us-nevada | 14,207 | clean |
| us-new-hampshire | 11,186 | clean |
| us-new-jersey | 28,145 | clean |
| us-new-mexico | 21,070 | clean |
| us-new-york | 42,871 | clean |
| us-north-carolina | 36,095 | clean |
| us-north-dakota | 14,275 | clean |
| us-ohio | 117,344 | clean |
| us-oklahoma | 13,752 | clean |
| us-oregon | 45,342 | clean |
| us-pennsylvania | 44,038 | clean |
| us-puerto-rico | 2,318 | clean |
| us-rhode-island | 6,360 | clean |
| us-south-carolina | 20,613 | clean |
| us-south-dakota | 11,975 | clean |
| us-tennessee | 22,002 | clean |
| us-texas | 82,221 | clean |
| us-utah | 22,027 | clean |
| us-vermont | 7,452 | clean |
| us-virgin-islands | 193 | clean |
| us-virginia | 33,844 | clean |
| us-washington | 51,195 | clean |
| us-west-virginia | 25,081 | clean |
| us-wisconsin | 84,094 | clean |
| us-wyoming | 14,739 | clean |
| uzbekistan | 35,052 | clean |
| vanuatu | 663 | clean |
| venezuela | 14,723 | clean |
| vietnam | 21,025 | clean |
| yemen | 7,008 | clean |
| zambia | 15,684 | clean |
| zimbabwe | 7,545 | clean |
| **Total** | 16,297,690 | |

[Back to the dataset root](./README.md)