The dataset viewer is not available for this split.
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 matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Plumbing Permit Dataset - Multi-City Building Systems Records - EmbedEarth
A geospatial open-data release of 223,000 plumbing permit records from 2018, 2019, 2020, 2021, 2022, 2023.
This dataset was created and distributed by EmbedEarth, programmable geographic infrastructure for searching, retrieving, and computing across the physical world.
The release contains records of permitted plumbing work, including reported projects involving water supply, drainage, fixtures, and related building systems. Plumbing permits provide a location-aware view of building maintenance, renovation, utility upgrades, and the work needed to keep properties operating. Records were normalized for consistent geospatial analysis while source-specific attributes remain available for inspection.
Search millions more geographic features
This Hugging Face release is a downloadable dataset from the broader EmbedEarth geographic index.
EmbedEarth provides developers and AI systems access to millions of geographic features and large collections of geolocated images and videos spanning infrastructure, places, roads, buildings, nature, community issues, and other physical-world observations.
Developers can search and work with geographic data programmatically through the EmbedEarth API, SDK, CLI, and MCP.
Build with EmbedEarth
- Geographic Feature List — browse geographic features available through EmbedEarth
- EmbedEarth — programmable infrastructure for Earth
- Developer Documentation — APIs, 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 search and spatial data from the terminal
- MCP — connect geographic search and spatial tools to AI agents
Dataset contents
This release contains 223,000 geolocated permit records from 2018, 2019, 2020, 2021, 2022, 2023. Each record represents a permit-related record reported by one of the contributing public sources. Where available, records include coordinates, geometry, permit dates, lifecycle status, project or work descriptions, source attribution, and source-specific properties.
This is a compiled public-records dataset, not a complete administrative inventory for every city or jurisdiction. Coverage reflects the contributing sources and their reporting practices.
Use cases
- building maintenance and renovation research
- utility and infrastructure planning
- permit pipeline analysis
- neighborhood change studies
- geospatial machine learning
- urban data applications
Schema
The downloadable Parquet and CSV files use a normalized schema. Source-specific permit attributes are stored as JSON inside properties rather than expanded into separate top-level columns. GeoJSON exposes the same record attributes alongside each record's geometry.
| Column | Type | Description |
|---|---|---|
id |
string | Stable identifier for the exported record. |
sample |
boolean | Whether this record was selected for an optional image sample. |
latitude |
float64 | Latitude in decimal degrees using WGS 84 when available. |
longitude |
float64 | Longitude in decimal degrees using WGS 84 when available. |
geometry_wkb |
binary | Geometry encoded as Well-Known Binary for spatial workflows. |
media_url |
string | URL for associated image or visual media when available. |
attribution |
string | Attribution information preserved from the contributing source. |
source |
string | Source or provider associated with the permit record. |
properties |
string | JSON-encoded object containing source-specific permit fields and metadata. |
Source-specific properties
The properties field preserves additional information associated with each permit record. Exact keys vary by source and record.
| Property group | Description |
|---|---|
permit_number / application_number |
Permit or application identifiers when supplied. |
permit_type / permit_class / work_type |
Local classifications and reported type of work. |
project_name / description / property_use |
Description of the project, work, or associated property. |
status / applied_at / issued_at / completed_at |
Lifecycle status and source milestone dates when supplied. |
address / city / state / postal_code |
Location text and administrative geography when supplied. |
applicant / contractor / owner_name |
Parties named by the source when supplied. |
valuation / units / floor_area |
Reported project measures when supplied. |
Not every property is populated for every record, and field names can differ between jurisdictions.
Download
The dataset is available in several formats:
Parquet is recommended for analytics, data science, and large-scale processing.
GeoJSON is useful for GIS software, spatial databases, web maps, and geographic visualization.
CSV provides a simple tabular representation for general analysis.
Build with the data
Geographic search
EmbedEarth Search lets developers search geographic features programmatically instead of relying only on pre-generated datasets.
Search can be used in maps, AI systems, research pipelines, monitoring tools, spatial applications, and geographic analysis workflows.
API, SDK, CLI, and MCP
Use the EmbedEarth developer documentation to connect permit records with other geographic features, regions, roads, buildings, infrastructure, and physical-world observations.
Example applications
Urban development intelligence
Map permit activity by place and time, compare reported work across jurisdictions, and combine permit records with buildings, roads, land use, neighborhoods, and other geographic context.
Change over time
Use the release years as a historical baseline for studying construction activity, redevelopment, maintenance, property change, and neighborhood evolution.
Spatial analysis
Join records to administrative boundaries, transit, parcels, infrastructure, or other geographic features for planning and research workflows.
Geographic AI
Give AI systems structured, location-aware context about reported permit activity while retaining source-specific details for inspection.
Methodology
The release combines public permit records from contributing municipal and regional open-data sources for 2018, 2019, 2020, 2021, 2022, 2023. Records are normalized into a common geospatial structure while source-specific values remain in properties.
Matching, classification, date interpretation, geocoding, and field availability can differ by source. Inclusion means that a record was present in a contributing public dataset; it does not mean that EmbedEarth independently verified the permit or the work described by the record.
Data sources and attribution
This dataset was prepared and distributed by EmbedEarth.
Original or contributing sources include:
- DOB NOW: Build Approved Permits
- Building Permits
- Los Angeles Building Permits 2020-Present
- Building and Safety - Building Permits Issued Between 2010 and 2019 (N)
- Building and Safety - Building Permits Issued Before 2010 (N)
- Licenses & Inspections Building and Zoning Permits
- CCS Permits
- Seattle Land Use Permits
- Issued Construction Permits
- Austin Open Data
- Chicago Data Portal
- DataLA
- DataSF
- NYC Open Data
- OpenDataPhilly
- Seattle Open Data
- Vancouver Open Data
Where available, record-level attribution, source information, and source URLs are preserved in the exported records. Users should retain applicable attribution and comply with the terms established by each original source.
License
Dataset compilation
This release is a compiled and normalized collection of public records. The source metadata does not provide one uniform machine-readable license covering every contributing jurisdiction, so the front matter uses other rather than asserting a single license for all underlying records.
EmbedEarth-created organization, normalization, and documentation should be attributed to EmbedEarth. A suggested attribution is:
EmbedEarth, “Plumbing Permit Records — 2018–2023,” https://embed.earth.
Third-party records
The compilation attribution above does not replace, expand, or override licenses, terms, attribution requirements, or access conditions attached to third-party records. Users are responsible for reviewing the linked source pages before redistribution or commercial use.
Limitations
Coverage depends on the contributing jurisdictions, their publication policies, their geographic coverage, and their update schedules. Field definitions, status values, date semantics, geocoding quality, and completeness vary across sources.
A permit record describes what a source reported. Permit issuance, approval, or publication should not be interpreted as proof that construction, demolition, installation, occupancy, or other work actually occurred. Applications requiring authoritative project or compliance decisions should independently verify the relevant record with the responsible jurisdiction.
Access millions more geographic features
This Hugging Face dataset contains 223,000 plumbing permit records, but it represents only one collection from EmbedEarth's broader geographic infrastructure.
EmbedEarth provides access to millions more geographic features and large collections of geolocated images and videos.
Search infrastructure, places, roads, buildings, environmental features, community issues, physical conditions, and other real-world observations programmatically through our API, SDK, CLI, and MCP.
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