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Datasets:
EmbedEarth
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uncontrolled-crosswalk

Modalities:
Geospatial
Tabular
Size:
1M<n<10M
Tags:
geospatial
open-data
tabular
pedestrian-safety
road-design
crosswalks
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
title: string
description: string
version: string
row_count: int64
files: struct<data.parquet: struct<path: string>, data.geojson: struct<path: string>, data.csv: struct<path (... 10 chars omitted)
  child 0, data.parquet: struct<path: string>
      child 0, path: string
  child 1, data.geojson: struct<path: string>
      child 0, path: string
  child 2, data.csv: struct<path: string>
      child 0, path: string
repository: string
publisher: string
to
{'version': Value('string'), 'title': Value('string'), 'publisher': Value('string'), 'repository': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              title: string
              description: string
              version: string
              row_count: int64
              files: struct<data.parquet: struct<path: string>, data.geojson: struct<path: string>, data.csv: struct<path (... 10 chars omitted)
                child 0, data.parquet: struct<path: string>
                    child 0, path: string
                child 1, data.geojson: struct<path: string>
                    child 0, path: string
                child 2, data.csv: struct<path: string>
                    child 0, path: string
              repository: string
              publisher: string
              to
              {'version': Value('string'), 'title': Value('string'), 'publisher': Value('string'), 'repository': Value('string')}
              because column names don't match

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Uncontrolled Crosswalk Dataset - Global Pedestrian Crossing Features - EmbedEarth

This dataset maps uncontrolled crosswalk features from OpenStreetMap. These are pedestrian crossings represented with road markings or crossing information but without traffic signals recorded for the crossing. They are useful for studying how people cross roads and how pedestrian infrastructure is represented in geographic data.

This release contains geolocated records from 2025 and is provided as a sample of a much larger dataset of mapped features.

Prepared and distributed by EmbedEarth from OpenStreetMap contributors.

Search millions more geographic features

This Hugging Face release is one downloadable dataset from the broader EmbedEarth geographic index. EmbedEarth provides developers and AI systems with tools for searching and working with geographic features, places, infrastructure, and other observations of the physical world.

Build with EmbedEarth

  • EmbedEarth — programmable infrastructure for Earth
  • Geographic Feature List — browse geographic features available through EmbedEarth
  • Developer Documentation — APIs, SDKs, tools, guides, and examples
  • Geographic Search SDK — search geographic features and regions programmatically
  • API — integrate geographic search and spatial data into applications
  • SDK — build geographic capabilities directly into applications
  • CLI — work with geographic data from the terminal
  • MCP — connect geographic search and spatial tools to AI agents

Search the physical world

The same geographic infrastructure used to create this dataset can support searches such as:

uncontrolled crosswalks near schools in Chicago
pedestrian crossings in Brooklyn
crosswalk coverage in Toronto
road crossings near transit stations in Seattle

Dataset overview

This dataset focuses on uncontrolled crosswalks represented in OpenStreetMap. Each record is a geolocated map feature with source attribution and, where available, additional tags such as names, addresses, references, operators, websites, access details, and feature-specific values.

Mapped pedestrian crossings with road markings but no traffic signal or other signal control recorded for the crossing feature.

Use cases

Pedestrian safety research

Study where uncontrolled crossings are mapped and how they relate to roads, traffic, schools, transit, and public facilities.

Street-design analysis

Compare crossing locations with road classes, sidewalks, signals, lighting, and neighborhood form.

Accessibility and mobility planning

Evaluate pedestrian networks and identify places where crossing infrastructure may need closer review.

Map enrichment

Add crossing context to routing, navigation, location intelligence, and city-analysis applications.

OpenStreetMap quality checks

Analyze coverage and consistency of crossing-related tags across places.

Schema

The downloadable Parquet and CSV files use a normalized schema. Source-specific attributes are preserved inside the JSON-encoded properties field rather than expanded into separate top-level columns. GeoJSON exposes the same record attributes alongside its geometry.

Column Type Description
id string Stable identifier for the exported record.
sample boolean Whether this record was selected for the optional image archive sample.
latitude float64 Latitude in decimal degrees using WGS 84 when a valid location is available.
longitude float64 Longitude in decimal degrees using WGS 84 when a valid location is available.
geometry_wkb binary The record geometry encoded as Well-Known Binary for spatial workflows.
media_url string URL for associated imagery or other visual media when available.
attribution string Attribution information carried into the exported record.
source string Source or provider associated with the observation.
properties string JSON-encoded object containing source-specific OpenStreetMap attributes.

Source-specific properties

The properties field preserves additional OpenStreetMap tags associated with each feature. Exact keys vary by record and region; common examples include:

Property Description
osm_id OpenStreetMap object identifier when supplied.
crossing / crossing:markings Crossing type and marking tags when mapped.
highway Road or path classification associated with the feature when supplied.
traffic_signals / lit Signal or lighting tags when mapped.
surface / tactile_paving Surface and accessibility tags when available.
name / ref Name or reference identifier when supplied.

Not every property is populated for every record.

Download

The dataset is available in Parquet, GeoJSON, and CSV formats:

Parquet is recommended for analytics, Python workflows, DuckDB, and large-scale processing. GeoJSON is useful for GIS software and web maps. CSV is convenient for tabular analysis and interoperability.

Data sources and attribution

This dataset was prepared and distributed by EmbedEarth from data contributed to OpenStreetMap.

OpenStreetMap data is available under the Open Database License (ODbL) 1.0. When using or redistributing the data, retain the OpenStreetMap attribution and follow the applicable ODbL requirements. Record-level media or third-party links may have additional terms set by their original providers.

Suggested attribution:

Contains information from OpenStreetMap, which is made available under the Open Database License (ODbL). https://www.openstreetmap.org/copyright

Methodology and limitations

Records were exported from an OpenStreetMap snapshot for 2025. The classification reflects mapped tags and may not capture every sign, beacon, signal, or field condition. It should be treated as a geographic discovery and analysis dataset rather than an engineering inventory or safety determination.

OpenStreetMap coverage and tagging vary by place and contributor. Geographic absence should not be interpreted as real-world absence, and mapped presence should not be treated as an independent inspection or operational certification.

Build with EmbedEarth

  • Geographic Feature List — browse geographic features available through EmbedEarth
  • EmbedEarth — programmable infrastructure for Earth
  • Developer Documentation — APIs, tools, guides, and examples
  • GitHub — EmbedEarth/e2 — open-source code, issues, and contributions
  • Geographic Search SDK — search geographic features and regions programmatically
  • API — integrate geographic search and spatial data into applications
  • SDK — build geographic capabilities directly into applications
  • CLI — work with geographic search and spatial data from the terminal
  • MCP — connect geographic search and spatial tools to AI agents

Install from npm:

npm i @embedearth/cli
npm i @embedearth/sdk
npm i @embedearth/mcp

License

The OpenStreetMap-derived database in this repository is made available under the Open Database License (ODbL) 1.0. See the ODbL license text and OpenStreetMap attribution guidance.

EmbedEarth-created explanatory text and metadata are provided to help users understand the release. Users are responsible for complying with the terms applying to OpenStreetMap data and any underlying third-party media or links.
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