Datasets:

EmbedEarth
/

pothole

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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Pothole Dataset - Global Road Surface Observations - EmbedEarth

This dataset contains geolocated observations selected for the semantic query “Pothole.” The records were retrieved because their source text, visual context, or metadata matched the query; this is a discovery-oriented collection rather than a complete administrative inventory. It is useful for exploratory mapping, visual search evaluation, geospatial research, and building retrieval or monitoring workflows around pothole. The release contains 10,811 records from 2023, 2024, 2025; matched-observation details are preserved in properties, with associated media available through media_url when supplied. Prepared and distributed by EmbedEarth from Mapillary and iNaturalist.

Dataset contents

Columns

The downloadable Parquet and CSV files use a normalized schema. Source-specific fields are not separate top-level columns; they are JSON-encoded in properties. GeoJSON exposes the same record attributes alongside its geometry.

Column	Type	Description
`id`	string	Stable identifier for the exported record.
`image_id`	string	Identifier of the record included in the optional image archive; null when no image was sampled.
`sample`	boolean	Whether this record was selected for the optional image archive sample.
`latitude`	float64	Latitude in decimal degrees (WGS 84) when a valid location is available.
`longitude`	float64	Longitude in decimal degrees (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 an associated image or other visual media when available.
`attribution`	string	Attribution text carried into the exported record.
`source`	string	Source or provider label carried into the exported record.
`properties`	string	A JSON-encoded object containing source-specific attributes; parse this field to access the original subject fields.

Source-specific properties

The properties column is a JSON-encoded object containing semantic retrieval attributes. The exact keys vary by contributing source; common examples are:

Property group	What it means
`address`	Address text when supplied by the matched observation.
`captured_at`	Timestamp of the visual or geospatial observation.
`source_url`	URL associated with the source observation when supplied.
`owner / publisher`	Source owner or publisher when supplied.
`year / month / season / time_of_day`	Temporal context derived from the observation when supplied.

Downloads

Data source and attribution

Prepared and distributed by EmbedEarth.

Original or contributing sources:

Use and limitations

Use it for exploratory mapping, visual search evaluation, geospatial research, and building retrieval or monitoring workflows around the query theme. It is a ranked semantic selection, not a complete administrative inventory of every occurrence.

No machine-readable license was supplied in the source metadata. Confirm the applicable terms from each contributing source before redistribution or commercial use.

Downloads last month: 30

Total file size:

47.7 MB