Datasets:

EmbedEarth
/

pothole

@@ -1,334 +1,80 @@
 ---
-pretty_name: "Pothole and Road Damage Observations 2023-2025"
-license: cc-by-sa-4.0
-size_categories: ["10K<n<100K"]
-tags: ["geospatial", "open-data", "tabular", "computer-vision", "pothole", "potholes", "road-damage", "road-infrastructure", "road-maintenance", "street-level-imagery", "visual-search", "semantic-search", "image-geolocation", "infrastructure", "urban", "mapping", "parquet", "geojson", "csv", "mapillary", "inaturalist", "year-2023", "year-2024", "year-2025"]
 ---
-# Pothole & Road Damage Dataset — 2023–2025
-A geospatial dataset of **10,811 pothole and road-damage observations** collected from geolocated street-level imagery and physical-world observations from 2023, 2024, and 2025.
-The dataset was created and distributed by **[EmbedEarth](https://embed.earth)**, programmable geographic infrastructure for searching, retrieving, and computing across the physical world.
-Observations were discovered using the semantic query **“Pothole”** and selected using available visual context, source text, metadata, temporal information, and geographic context.
-This dataset can be used for **pothole detection, road damage analysis, computer vision, infrastructure monitoring, road maintenance research, geospatial AI, urban planning, and visual search**.
-## Search millions more geographic features with EmbedEarth
-This Hugging Face dataset represents one downloadable query from the broader **[EmbedEarth](https://embed.earth)** geographic index.
-EmbedEarth gives developers and AI systems access to **millions of geographic features and large collections of geolocated images and videos** covering roads, infrastructure, buildings, places, community issues, nature, and other features of the physical world.
-Instead of relying only on static downloadable datasets, you can search and work with geographic data programmatically.
-### Build with EmbedEarth
-* **[Geographic Feature List](https://www.embed.earth/catalog/features)** — browse geographic features available through EmbedEarth
-* **[EmbedEarth](https://embed.earth)** — programmable infrastructure for Earth
-* **[Developer Documentation](https://www.embed.earth/docs)** — Search, Compute, Routing, Geocoding, Maps, Data, SDK, CLI, and MCP
-* **[Geographic Search SDK](https://www.embed.earth/docs/sdk/search)** — query geographic features and regions programmatically
-* **API** — add geographic search and spatial data to applications and data pipelines
-* **SDK** — build geographic capabilities directly into JavaScript and TypeScript applications
-* **CLI** — search and work with physical-world data from the terminal
-* **MCP** — give AI agents access to geographic search and spatial tools
-## Search the physical world
-The same geographic infrastructure used to build this pothole dataset can be used to find many other physical-world conditions and features.
-Example searches include:
-```text
-potholes in New York
-potholes in Los Angeles
-road damage in California
-damaged pavement in Toronto
-cracked roads in Chicago
-road construction in Manhattan
-damaged sidewalks in Brooklyn
-traffic accidents in Texas
-construction permits in Miami
-fallen trees in Florida
-crosswalks in San Francisco
-utility poles in Houston
-```
-See the **[EmbedEarth Search SDK documentation](https://www.embed.earth/docs/sdk/search)** to query published geographic features by area, coordinates, bounding box, attributes, and other spatial constraints.
-## Dataset overview
-This release contains **10,811 geolocated pothole-related observations** covering:
-* 2023
-* 2024
-* 2025
-Each record was selected because available imagery, metadata, text, or geographic context matched the pothole query.
-Where available, observations include:
-* latitude and longitude
-* spatial geometry
-* observation timestamps
-* street-level imagery or media URLs
-* source attribution
-* source URLs
-* source-specific metadata
-* temporal context
-The dataset is designed for **geographic discovery and analysis** rather than as a complete government or municipal inventory of every pothole.
-## What can this pothole dataset be used for?
-### Pothole detection
-Use the observations as part of research or evaluation workflows for identifying potholes and damaged pavement from street-level imagery.
-### Road damage detection
-Explore visual and geographic indicators of road deterioration, pavement defects, surface damage, and transportation infrastructure conditions.
-### Computer vision
-Use pothole observations in computer vision and multimodal research involving street imagery, physical-world understanding, visual retrieval, or image geolocation.
-### Road maintenance research
-Analyze where potholes and visible road defects occur to support road maintenance research, infrastructure prioritization, or exploratory transportation analysis.
-### Geospatial machine learning
-Combine pothole observations with roads, weather, traffic, land use, neighborhoods, census information, road classifications, or other spatial datasets.
-### Urban infrastructure monitoring
-Build systems that retrieve or monitor visible changes to roads and other public infrastructure over time.
-### Geographic AI
-Use physical-world observations as geographic context for AI agents, multimodal systems, spatial reasoning models, and mapping applications.
-## Dataset schema
-The downloadable Parquet and CSV files use a normalized schema.
-Source-specific attributes are preserved inside the JSON-encoded `properties` field rather than being expanded into many source-dependent top-level columns.
-The GeoJSON release exposes the same attributes alongside geographic geometry.
-| Column         | Type    | Description                                                                            |
-| -------------- | ------- | -------------------------------------------------------------------------------------- |
-| `id`           | string  | Stable identifier for the exported observation.                                        |
-| `image_id`     | string  | Identifier associated with the optional image archive; null when no image was sampled. |
-| `sample`       | boolean | Whether the observation was selected for the optional image archive 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  | Geographic geometry encoded as Well-Known Binary for spatial workflows.                |
-| `media_url`    | string  | URL for associated street-level imagery or other visual media when available.          |
-| `attribution`  | string  | Attribution information preserved from the contributing source.                        |
-| `source`       | string  | Source or provider associated with the observation.                                    |
-| `properties`   | string  | JSON-encoded object containing source-specific attributes and retrieval metadata.      |
-## Source-specific properties
-The `properties` field preserves additional metadata associated with each pothole observation.
-The exact fields vary by source and record.
-| Property            | Description                                                                         |
-| ------------------- | ----------------------------------------------------------------------------------- |
-| `address`           | Address or geographic location text associated with the observation when available. |
-| `captured_at`       | Timestamp of the imagery or geographic observation.                                 |
-| `source_url`        | URL associated with the original source observation when supplied.                  |
-| `owner / publisher` | Original owner, contributor, or publisher when supplied.                            |
-| `year`              | Year associated with the observation.                                               |
-| `month`             | Month associated with the observation when available.                               |
-| `season`            | Seasonal context associated with or derived from the observation.                   |
-| `time_of_day`       | Time-of-day context when available.                                                 |
-Not every property is populated for every observation.
-## Download the pothole dataset
-The dataset is available in multiple formats for geospatial, machine-learning, and data-analysis workflows.
-* [Parquet](https://huggingface.co/datasets/embedearth/pothole/resolve/main/data.parquet)
-* [GeoJSON](https://huggingface.co/datasets/embedearth/pothole/resolve/main/data.geojson)
-* [CSV](https://huggingface.co/datasets/embedearth/pothole/resolve/main/data.csv)
-### Parquet
-Recommended for analytics, Python workflows, DuckDB, data science, and large-scale processing.
-### GeoJSON
-Recommended for GIS software, spatial databases, MapLibre, Leaflet, deck.gl, web maps, and other geographic applications.
-### CSV
-Recommended for simple tabular analysis, spreadsheets, and general-purpose data workflows.
-## Build applications with EmbedEarth
-### Geographic Search
-**[EmbedEarth Search](https://www.embed.earth/docs/sdk/search)** lets developers query published geographic features across areas and receive the results as geographic data.
-Search can be constrained using geographic areas, coordinates, bounding boxes, properties, columns, and result limits.
-Use it to power:
-* geographic search engines
-* mapping applications
-* infrastructure analysis
-* AI agents
-* location intelligence
-* spatial research
-* monitoring systems
-* geospatial data pipelines
-### JavaScript and TypeScript SDK
-EmbedEarth provides an SDK for building spatial search, routing, compute, mapping, and data workflows directly into applications.
-**[View the EmbedEarth documentation](https://www.embed.earth/docs)**
-### API
-Use EmbedEarth programmatically from applications, backend services, AI systems, and data pipelines.
-The platform includes geographic capabilities for Search, Compute, Routing, Geocoding, Maps, and Data.
-### CLI
-Use EmbedEarth directly from the terminal for geographic search, local data workflows, routing, compute, and spatial development.
-### MCP for AI agents
-EmbedEarth can expose geographic capabilities to MCP-compatible AI agents.
-This allows agents to work with dedicated geographic infrastructure instead of relying exclusively on general model knowledge when answering questions about places and the physical world.
-## Example applications
-### Road condition mapping
-Map pothole observations and visible pavement damage across cities, neighborhoods, transportation corridors, or other geographic regions.
-### Pothole detection models
-Use the dataset as part of experimentation or evaluation for visual systems designed to recognize potholes and other road-surface defects.
-### Infrastructure inspection
-Combine street-level observations with roads and other infrastructure data to identify areas that may warrant closer inspection.
-### Road maintenance prioritization
-Analyze the geographic distribution of pothole observations alongside traffic, road class, population, weather, or municipal data.
-### Smart city applications
-Build applications that combine road conditions with service requests, traffic accidents, permits, construction activity, and other urban data.
-### Physical-world search
-Use natural-language or feature-based retrieval to discover conditions and objects that exist at physical locations.
-## Methodology
-Observations were retrieved through semantic geographic search around the query:
-**“Pothole”**
-Matching may incorporate available:
-* street-level visual context
-* textual descriptions
-* source metadata
-* temporal metadata
-* geographic context
-The dataset prioritizes relevant, discoverable pothole observations rather than exhaustive geographic coverage.
-Inclusion in the dataset indicates semantic relevance to the pothole query. It does not necessarily mean the observation has been independently inspected or formally classified as a pothole by a transportation authority or civil engineer.
-## Data sources and attribution
-This dataset was prepared and distributed by **[EmbedEarth](https://embed.earth)**.
-Original or contributing sources include:
-* [Mapillary](https://www.mapillary.com/app/)
-* [iNaturalist](https://www.inaturalist.org/)
-Where available, record-level source information, attribution, and source URLs are preserved in the exported dataset.
-Users accessing or using individual media assets should retain applicable attribution and comply with source-specific terms.
-## License
-### Dataset compilation
-The **EmbedEarth dataset compilation, organization, selection, and EmbedEarth-created metadata** in this repository are licensed under the **Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)**.
-You may:
-* share and redistribute the licensed dataset material
-* remix, transform, and build upon it
-* use the licensed material for commercial or non-commercial purposes
-Subject to the following conditions:
-* **Attribution** — provide appropriate credit to EmbedEarth and, where applicable, the original source or contributor.
-* **ShareAlike** — adaptations of material covered by this license must be distributed under CC BY-SA 4.0 or a compatible license.
-Suggested attribution:
-> EmbedEarth, “Pothole & Road Damage Dataset — 2023–2025,” https://embed.earth, licensed under CC BY-SA 4.0.
-### Third-party content
-The CC BY-SA 4.0 license applied to the EmbedEarth dataset compilation **does not replace, expand, or override licenses attached to third-party content**.
-Images, videos, observations, source metadata, links, and other third-party materials referenced by this dataset may remain subject to separate licenses, attribution requirements, or usage restrictions established by their original creators or source platforms.
-Users are responsible for complying with the applicable terms of any underlying third-party content they access or use.
-Where available, attribution and source information are preserved in the exported records.
-## Limitations
-This is a **discovery-oriented pothole dataset**, not an authoritative or comprehensive road-condition inventory.
-Geographic coverage depends on the availability of source observations and imagery. Coverage may vary significantly between cities, regions, and countries.
-The dataset can contain:
-* incomplete metadata
-* uneven geographic coverage
-* duplicate or nearby observations
-* ambiguous road defects
-* false-positive semantic matches
-* observations whose road condition has since changed
-Applications requiring authoritative road-condition assessments should independently validate relevant observations.
----
-## Need more than a static pothole dataset?
-This Hugging Face release contains **10,811 pothole observations**, but potholes are only one physical-world feature available through EmbedEarth.
-**[EmbedEarth](https://embed.earth)** provides access to **millions more geographic features and large collections of geolocated images and videos**.
-Search roads, infrastructure, places, buildings, community issues, natural features, physical conditions, and other real-world observations programmatically using the EmbedEarth API, SDK, CLI, and MCP.
-**[Explore EmbedEarth →](https://embed.earth)**
-**[Read the developer documentation →](https://www.embed.earth/docs)**
-**[Search geographic features with the SDK →](https://www.embed.earth/docs/sdk/search)**

 ---
+tags:
+  - geospatial
+  - open-data
+  - tabular
+  - parquet
+  - geojson
+  - csv
+  - pothole
+  - query
+  - semantic-search
+  - image-geolocation
+  - year-2023
+  - year-2024
+  - year-2025
+  - mapillary
+  - inaturalist
+libraries:
+  - datasets
+size_categories:
+  - 10K<n<100K
+pretty_name: "Pothole Dataset - Global Road Surface Observations - EmbedEarth"
 ---
+# 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
+- [Parquet](https://s3.us-east-005.backblazeb2.com/open-datasets/pothole/2023,2024,2025/e7b1e941-b6ae-4d36-b014-8e1f1c8d8652/data.parquet)
+- [GeoJSON](https://s3.us-east-005.backblazeb2.com/open-datasets/pothole/2023,2024,2025/e7b1e941-b6ae-4d36-b014-8e1f1c8d8652/data.geojson)
+- [CSV](https://s3.us-east-005.backblazeb2.com/open-datasets/pothole/2023,2024,2025/e7b1e941-b6ae-4d36-b014-8e1f1c8d8652/data.csv)
+- [Manifest](https://s3.us-east-005.backblazeb2.com/open-datasets/pothole/2023,2024,2025/e7b1e941-b6ae-4d36-b014-8e1f1c8d8652/manifest.json)
+- [Image archive](https://s3.us-east-005.backblazeb2.com/open-datasets/pothole/2023,2024,2025/e7b1e941-b6ae-4d36-b014-8e1f1c8d8652/images.zip)
+## Data source and attribution
+Prepared and distributed by [EmbedEarth](https://embed.earth).
+Original or contributing sources:
+- [Mapillary](https://www.mapillary.com/app/)
+- [iNaturalist](https://www.inaturalist.org/)
+## 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.