| ---
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| license: mit
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| task_categories:
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| - object-detection
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| tags:
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| - curb-ramp
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| - accessibility
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| - streetscape
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| - open-government-data
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| - reproducibility
|
| ---
|
|
|
| # RampNet Stage 1 Inputs
|
|
|
| The **inputs** to the RampNet Stage 1 pipeline, from **RampNet: A Two-Stage Pipeline for
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| Bootstrapping Curb Ramp Detection in Streetscape Images from Open Government Metadata**
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| (O'Meara et al., ICCV'25 CV4A11y workshop, [arXiv:2508.09415](https://arxiv.org/abs/2508.09415)).
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|
|
| [`projectsidewalk/rampnet-dataset`](https://huggingface.co/datasets/projectsidewalk/rampnet-dataset)
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| is what Stage 1 *produced* — 214k annotated panoramas. **This is what went in.**
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|
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| `v1.0-iccv2025` shipped the Stage 1 code without these files. That is not a gap a re-download can
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| close: the city open-data portals serve *current* inventories and they drift, so a file fetched
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| today is a different experiment rather than a copy of this one.
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|
|
| ## Contents
|
|
|
| | file | bytes | sha256 |
|
| | :--- | ---: | :--- |
|
| | `location_data/bend.geojson` | 14,434,722 | `a0da4e016474c2c8fddcc6f77a7dd4a3aa5caaea455c839fad762d66a7af948e` |
|
| | `location_data/nyc.csv` | 42,057,860 | `beea2b323d00d82192dd18ace3f257cef30ce3b579544d4e607fe7abe5e57f8c` |
|
| | `location_data/portland.geojson` | 15,326,478 | `d5366a7e0d18f09f9ba49f1cbf7a26b99ee90633689dbe94cbde2a21bd395dbe` |
|
| | `manifests/finaldataset.jsonl` | 64,293,017 | `cf2e709237f26d5e4883f22f6de635276de3049e20612dfabe33ab1f6c06fd7b` |
|
| | `manifests/dataset.jsonl` | 59,087,253 | `4ca16dca507aed43685ab842bc4cf22bc472edbd87c3f484594adb231edd2121` |
|
| | `manifests/negativepanosSHORTENED.jsonl` | 5,205,764 | `bd1cbf00fcb09469cc6b268c0aa0053c13352efc66bb0fb5a761d40cd3654b4b` |
|
| | `manifests/negativepanos.jsonl` | 10,466,000 | `f7f1ac9c26a567419100d38dc7a4096d39fa5d80e70d61cb6a5e5be944f4bfa8` |
|
| | `manifests/all_locations.csv` | 13,452,021 | `06fec4e9a8077582deac12c3c303b89c8a2396ce3d78e7e923b0960a2c091a3b` |
|
| | `street_data/Bend - Streets.geojson` | 8,934,761 | `1c5cf897feb43bf8668d4272f71a130609f727ad4798d4145c837139e8e07311` |
|
| | `street_data/New York - Streets.geojson` | 669,049,016 | `946e13a4fb63be2d1a1dd15bca3a9a60c84965cef3a67cb8aae73922b51868bb` |
|
| | `street_data/Portland - Streets.geojson` | 123,616,787 | `7675f0936e0e194c920957c8921352ae73ff788417d4ad3b4666c1f53d5938e3` |
|
| | **total** | **1,025,923,679** | |
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|
|
| Every file above is byte-identical to the artifacts recovered from the cluster storage that held
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| the paper's run. The sha256 values are also recorded in
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| [`docs/data_provenance.md`](https://github.com/ProjectSidewalk/RampNet/blob/main/docs/data_provenance.md).
|
|
|
| ### `manifests/` — the reproduction path, and the most important part here
|
|
|
| | file | what it is |
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| | :--- | :--- |
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| | `finaldataset.jsonl` | the exact **219,170-panorama** manifest `download_dataset.py` consumed |
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| | `dataset.jsonl` | the 175,336 positive panoramas, with the ramp coordinates that landed in each |
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| | `negativepanosSHORTENED.jsonl` | the **43,834 negatives actually used** (exactly 20.0% of the final set) |
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| | `negativepanos.jsonl` | the full 88,125-candidate negative pool it was drawn from |
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| | `all_locations.csv` | the three inventories merged to `(latitude, longitude, date)` |
|
|
|
| `finaldataset.jsonl` = `dataset.jsonl` + `negativepanosSHORTENED.jsonl`, which is worth stating
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| because the repo describes that merge as a manual step.
|
|
|
| ### `location_data/` and `street_data/` — the sources
|
|
|
| `location_data/` holds the three government curb ramp inventories. `street_data/` holds the street
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| centrelines used to sample negative panorama locations; these are the **full downloads**, whereas
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| the training repo commits an 18.7 MB derivative carrying only the geometry and name field the
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| pipeline reads (proven to yield an identical sampling network by
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| `scripts/build_street_derivative.py verify`).
|
|
|
| ## Three things these files cannot give you
|
|
|
| Published beside the data rather than discovered later:
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|
|
| 1. **The paper's row order is unreproducible.** `combine_location_data.py` shuffled
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| `all_locations.csv` with **no seed** — `random.seed(42)` was added afterwards. The row
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| *contents* are intact, which is why provenance can still be recovered by coordinate join
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| (`scripts/analysis/gov_provenance.py`), but the ordering is gone.
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| 2. **The negatives cannot be regenerated, only downloaded.** `generate_negative_panos.py` samples
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| street locations with an unseeded RNG. `negativepanosSHORTENED.jsonl` is the *only* record of
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| which negatives the paper used — that is why it is here.
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| 3. **Date semantics changed after the paper.** The paper-era `convert_date` mapped an unknown
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| install date to `"2000-01-01"`, so every undated ramp trivially passed the
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| "installed before the panorama was captured" check; the current code returns `""`. Measured,
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| **23,088 records (8.36%)** have no install date, which bounds how differently a re-run from
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| current `main` would select.
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|
|
| ## Which government records became training labels
|
|
|
| `scripts/analysis/gov_provenance.py` in the training repo rebuilds the mapping from each
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| `all_locations.csv` row back to its source file and government ID, and verifies it —
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| **276,071 / 276,071 rows resolved, 0 unmatched**.
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|
|
| | | Bend | Portland | NYC | total |
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| | :--- | ---: | ---: | ---: | ---: |
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| | government records | 13,357 | 45,035 | 217,679 | 276,071 |
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| | consumed by a generated panorama | 5,110 | 21,075 | 130,527 | **156,712** |
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| | consumption rate | 38.26% | 46.80% | 59.96% | **56.77%** |
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|
|
| So **43.23% never became a training label**, mostly because no panorama resolved for the location
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| or the install date failed the predates-capture check.
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|
|
| ## Usage
|
|
|
| This is a set of source documents, not a row-iterable dataset — `load_dataset()` will not work.
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| Download it directly:
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|
|
| ```bash
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| hf download projectsidewalk/rampnet-stage1-inputs --repo-type dataset --local-dir rampnet-stage1-inputs
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| ```
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|
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| Then place the pieces where the pipeline expects them, under
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| `stage_one/dataset_generation/` in [the training repo](https://github.com/ProjectSidewalk/RampNet):
|
|
|
| ```
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| location_data/ -> stage_one/dataset_generation/location_data/ (also already in git)
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| street_data/ -> stage_one/dataset_generation/street_data/
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| manifests/*.jsonl,csv -> stage_one/dataset_generation/
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| ```
|
|
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| You will also need the crop model,
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| [`projectsidewalk/rampnet-crop-model`](https://huggingface.co/projectsidewalk/rampnet-crop-model) —
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| Stage 1 does not run without it.
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|
|
| ## Provenance
|
|
|
| | Field | Value |
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| | :--- | :--- |
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| | Pipeline code | https://github.com/ProjectSidewalk/RampNet @ `799e3e5` |
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| | Exported | 2026-08-04 by `scripts/export_stage1_inputs.py` |
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| | Replication ledger | [`docs/replication.md`](https://github.com/ProjectSidewalk/RampNet/blob/main/docs/replication.md) |
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|
|
| ## Citation
|
|
|
| ```bibtex
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| @inproceedings{omeara2025rampnet,
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| author = {John S. O'Meara and Jared Hwang and Zeyu Wang and Michael Saugstad and Jon E. Froehlich},
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| title = {{RampNet: A Two-Stage Pipeline for Bootstrapping Curb Ramp Detection in Streetscape Images from Open Government Metadata}},
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| booktitle = {{ICCV'25 Workshop on Vision Foundation Models and Generative AI for Accessibility: Challenges and Opportunities (ICCV 2025 Workshop)}},
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| year = {2025},
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| doi = {https://doi.org/10.48550/arXiv.2508.09415},
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| }
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| ```
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|
|
