---
license: mit
task_categories:
- object-detection
tags:
- curb-ramp
- accessibility
- streetscape
- open-government-data
- reproducibility
---

# RampNet Stage 1 Inputs

The **inputs** to the RampNet Stage 1 pipeline, from **RampNet: A Two-Stage Pipeline for
Bootstrapping Curb Ramp Detection in Streetscape Images from Open Government Metadata**
(O'Meara et al., ICCV'25 CV4A11y workshop, [arXiv:2508.09415](https://arxiv.org/abs/2508.09415)).

[`projectsidewalk/rampnet-dataset`](https://huggingface.co/datasets/projectsidewalk/rampnet-dataset)
is what Stage 1 *produced* — 214k annotated panoramas. **This is what went in.**

`v1.0-iccv2025` shipped the Stage 1 code without these files. That is not a gap a re-download can
close: the city open-data portals serve *current* inventories and they drift, so a file fetched
today is a different experiment rather than a copy of this one.

## 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** | |

Every file above is byte-identical to the artifacts recovered from the cluster storage that held
the paper's run. The sha256 values are also recorded in
[`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 |
| :--- | :--- |
| `finaldataset.jsonl` | the exact **219,170-panorama** manifest `download_dataset.py` consumed |
| `dataset.jsonl` | the 175,336 positive panoramas, with the ramp coordinates that landed in each |
| `negativepanosSHORTENED.jsonl` | the **43,834 negatives actually used** (exactly 20.0% of the final set) |
| `negativepanos.jsonl` | the full 88,125-candidate negative pool it was drawn from |
| `all_locations.csv` | the three inventories merged to `(latitude, longitude, date)` |

`finaldataset.jsonl` = `dataset.jsonl` + `negativepanosSHORTENED.jsonl`, which is worth stating
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
centrelines used to sample negative panorama locations; these are the **full downloads**, whereas
the training repo commits an 18.7 MB derivative carrying only the geometry and name field the
pipeline reads (proven to yield an identical sampling network by
`scripts/build_street_derivative.py verify`).

## Three things these files cannot give you

Published beside the data rather than discovered later:

1. **The paper's row order is unreproducible.** `combine_location_data.py` shuffled
   `all_locations.csv` with **no seed** — `random.seed(42)` was added afterwards. The row
   *contents* are intact, which is why provenance can still be recovered by coordinate join
   (`scripts/analysis/gov_provenance.py`), but the ordering is gone.
2. **The negatives cannot be regenerated, only downloaded.** `generate_negative_panos.py` samples
   street locations with an unseeded RNG. `negativepanosSHORTENED.jsonl` is the *only* record of
   which negatives the paper used — that is why it is here.
3. **Date semantics changed after the paper.** The paper-era `convert_date` mapped an unknown
   install date to `"2000-01-01"`, so every undated ramp trivially passed the
   "installed before the panorama was captured" check; the current code returns `""`. Measured,
   **23,088 records (8.36%)** have no install date, which bounds how differently a re-run from
   current `main` would select.

## Which government records became training labels

`scripts/analysis/gov_provenance.py` in the training repo rebuilds the mapping from each
`all_locations.csv` row back to its source file and government ID, and verifies it —
**276,071 / 276,071 rows resolved, 0 unmatched**.

| | Bend | Portland | NYC | total |
| :--- | ---: | ---: | ---: | ---: |
| government records | 13,357 | 45,035 | 217,679 | 276,071 |
| consumed by a generated panorama | 5,110 | 21,075 | 130,527 | **156,712** |
| consumption rate | 38.26% | 46.80% | 59.96% | **56.77%** |

So **43.23% never became a training label**, mostly because no panorama resolved for the location
or the install date failed the predates-capture check.

## Usage

This is a set of source documents, not a row-iterable dataset — `load_dataset()` will not work.
Download it directly:

```bash
hf download projectsidewalk/rampnet-stage1-inputs --repo-type dataset --local-dir rampnet-stage1-inputs
```

Then place the pieces where the pipeline expects them, under
`stage_one/dataset_generation/` in [the training repo](https://github.com/ProjectSidewalk/RampNet):

```
location_data/          -> stage_one/dataset_generation/location_data/     (also already in git)
street_data/            -> stage_one/dataset_generation/street_data/
manifests/*.jsonl,csv   -> stage_one/dataset_generation/
```

You will also need the crop model,
[`projectsidewalk/rampnet-crop-model`](https://huggingface.co/projectsidewalk/rampnet-crop-model) —
Stage 1 does not run without it.

## Provenance

| Field | Value |
| :--- | :--- |
| Pipeline code | https://github.com/ProjectSidewalk/RampNet @ `799e3e5` |
| Exported | 2026-08-04 by `scripts/export_stage1_inputs.py` |
| Replication ledger | [`docs/replication.md`](https://github.com/ProjectSidewalk/RampNet/blob/main/docs/replication.md) |

## Citation

```bibtex
@inproceedings{omeara2025rampnet,
  author    = {John S. O'Meara and Jared Hwang and Zeyu Wang and Michael Saugstad and Jon E. Froehlich},
  title     = {{RampNet: A Two-Stage Pipeline for Bootstrapping Curb Ramp Detection in Streetscape Images from Open Government Metadata}},
  booktitle = {{ICCV'25 Workshop on Vision Foundation Models and Generative AI for Accessibility: Challenges and Opportunities (ICCV 2025 Workshop)}},
  year      = {2025},
  doi       = {https://doi.org/10.48550/arXiv.2508.09415},
}
```