--- pretty_name: "New York City Potholes" license: other license_name: kled-commercial-training-license license_link: https://www.kled.ai/datasets/capture-nyc-street-potholes task_categories: - object-detection - image-classification tags: - kled - commercial-license - consented-data - human-collected - sample-on-request - image - real-world-capture - phone-capture - united-states - raw - road-condition - municipal annotations_creators: - no-annotation language_creators: - crowdsourced source_datasets: - original viewer: false gated: manual extra_gated_heading: "Request a sample set of New York City Potholes" extra_gated_prompt: "Kled delivers sample sets and licensed data by email, not through Hugging Face. Tell us a little about your work and the Kled data team will send a sample set and pricing. Your request goes to team@kled.ai." extra_gated_fields: Company or lab: text Role: text What are you building: text Volume you need: type: select options: - Sample set only - Under 10,000 items - 10,000 to 100,000 items - 100,000 or more items - Full dataset - Custom collection on the Kled contributor base I agree to be contacted by Kled about this request: checkbox extra_gated_button_content: "Request a sample set" extra_gated_description: "Prefer email? Write to team@kled.ai with the dataset name in the subject line." --- # Dataset Card for New York City Potholes

New York City Potholes

> In-app photos of potholes on New York City streets, location-verified, some with a second shot of the street. Collected through the Kled app by consenting contributors and licensed for commercial training. The files are delivered by Kled under license, not downloaded from this repository. Use **Request a sample set** at the top of this page, or write to [team@kled.ai](mailto:team@kled.ai). Catalog entry: [kled.ai/datasets/capture-nyc-street-potholes](https://www.kled.ai/datasets/capture-nyc-street-potholes?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=capture-nyc-street-potholes). ## Dataset Details ### Dataset Description Potholes photographed in-app by contributors whose location was verified in New York City, some with a second frame showing the street around the defect. No severity or dimension labels; where a second frame exists, the pair supports defect classification and localization in the scene, and the single-city scope means surface types, markings and lighting are consistent enough to study the defect and not the city. RDD2022, the standard road-damage benchmark, is 47,000 images across six countries under a ShareAlike license; this is a single-city, pedestrian-level counterpart with a commercial license, and one of the best-mapped cities on earth is a useful calibration point before road-condition models meet less-documented streets. - **Curated by:** Kled ([useKled](https://huggingface.co/useKled)) - **Collected through:** Phone camera (in app) - **Regions:** New York City - **Volume:** Available on request - **Contributors:** Available on request - **Modality:** Images - **Format:** JPG / PNG / HEIC - **Resolution:** Native phone camera resolution (JPG, PNG, HEIC) - **Annotations:** Raw (no labels), location-verified - **Quality control:** 99%+ passed QA review - **License:** Non-exclusive commercial training license ([terms](https://www.kled.ai/datasets/capture-nyc-street-potholes?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=capture-nyc-street-potholes)); exclusivity available on request - **Consent:** Each contributor opted in to this use when they uploaded. ### Dataset Sources - **Catalog page:** [kled.ai/datasets/capture-nyc-street-potholes](https://www.kled.ai/datasets/capture-nyc-street-potholes?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=capture-nyc-street-potholes) - **Sample set:** through the access request at the top of this page, or [team@kled.ai](mailto:team@kled.ai) - **Publisher:** [huggingface.co/useKled](https://huggingface.co/useKled) ## Uses ### Direct Use - Road-condition monitoring - Municipal maintenance prioritization - Pavement defect detection and localization ### Out-of-Scope Use Redistribution or resale of the files, use outside the terms of the commercial training license, and any attempt to identify or contact contributors or people who appear in the data. Uses that require labels this set does not carry should be scoped with the data team first. ## Dataset Structure ### Data Instances One item per record, delivered as JPG / PNG / HEIC. Volume: Available on request. ### Data Fields - **File:** the JPG / PNG / HEIC file as captured or uploaded by the contributor - **Labels:** none; the set is delivered raw. Labeling on the same items is available on request. - **Capture metadata:** capture tool, country of capture, submission time - **Review status:** QA outcome (99%+ passed QA review) Field names and the manifest format are confirmed with the sample set. ### Data Splits Delivered as one set. Subsets by country, language or label, and custom splits, are available on request. ## Dataset Creation ### Curation Rationale The dataset exists because the public alternatives named in the description are non-commercial, synthetic, small or collected elsewhere. Kled runs the task as a paid brief in its app so that the data is captured on purpose, under the capture rules above, by people who have opted in to commercial use. ### Source Data #### Data Collection and Processing Contributors complete the task in the Kled app. Capture tool: Phone camera (in app). Each submission is reviewed against the task brief; submissions that break the capture rules are rejected. 99%+ passed QA review. Files are delivered as JPG / PNG / HEIC, in the resolution noted above. #### Who are the source data producers? Kled contributors, who complete paid data tasks on their own phones. This task was open to contributors in New York City. Kled has 625,000+ contributors in 100+ countries, strongest in Southeast Asia, the United States and Europe. ### Annotations #### Annotation process No annotations are included. Items are delivered as captured; labeling on the same items can be scoped with the data team. #### Who are the annotators? Not applicable; no annotations are included. #### Personal and Sensitive Information Every item was contributed by its owner, who opted in to this use at upload. The task brief states what may and may not appear in a submission, and reviewers reject submissions that break those rules. Kled's data handling practices are documented at [trust.kled.ai](https://trust.kled.ai/); questions about handling requirements for your use go to [team@kled.ai](mailto:team@kled.ai). ## Bias, Risks, and Limitations Coverage follows the contributor base: strongest in Southeast Asia, the United States and Europe, thinner elsewhere, and limited to New York City for this task. Devices, settings and conditions vary by contributor, which is the point of the collection and also means no two items are captured under identical conditions. Where labels are contributor-entered, they are checked at review but are not expert annotations. Volumes are the current scope of the task and grow as collection continues. ### Recommendations Request a sample set and run it against your task before licensing. Ask for per-country or per-language counts where they matter to your use. ## Licensing Information Non-exclusive commercial training license; exclusivity on request. Each contributor opted in to this use when they uploaded. The license terms are on the [catalog page](https://www.kled.ai/datasets/capture-nyc-street-potholes?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=capture-nyc-street-potholes). ## Citation ```bibtex @misc{kled_capture_nyc_street_potholes_2026, title = {New York City Potholes}, author = {Kled}, year = {2026}, url = {https://www.kled.ai/datasets/capture-nyc-street-potholes}, note = {Commercially licensed dataset collected through the Kled app. Sample sets on request.} } ``` ## Dataset Card Contact Kled data team: [team@kled.ai](mailto:team@kled.ai) ## Related Kled datasets - [AI Images in the Wild (250,000 screens)](https://huggingface.co/datasets/useKled/ai-generated-images-found-in-the-wild) - [Six-View Product Photos (10,000 products)](https://huggingface.co/datasets/useKled/all-six-faces-of-one-package) - [App Error Screens (10,000 screens)](https://huggingface.co/datasets/useKled/app-error-screens) - [Inner App Settings (100,000 screens)](https://huggingface.co/datasets/useKled/app-settings-screens)