---
license: mit
tags:
- image-feature-extraction
- visual-place-recognition
- event-cameras
- geolocalization
---

# Springfield-Event

Springfield-Event is an evaluation dataset for event-based visual place
recognition. It contains raw events from a Prophesee EVK4, synchronised GPS and
phone telemetry, seven database traversals with different viewing directions,
and 5,557 query windows captured during day and dawn conditions in Springfield,
Queensland, Australia.

The dataset is evaluation-only and does not define training or validation
splits.

## At a glance

| | |
| --- | --- |
| Sensor | Prophesee EVK4, 1280 x 720 |
| Database | 7 sessions, 115 HDF5 partitions |
| Gallery | 132,569 valid 50 ms windows |
| Queries | 5,557 selected 50 ms windows from 38 passes |
| Conditions | Day and dawn |
| Ground truth | GPS distance <= 25 m |
| Download size | Approximately 810 GiB |

## Organisation

Keep the downloaded directory in the following form. In particular,
`query_slices.csv` belongs directly inside `queries/`.

```text
springfield/
|-- database/
|   `-- <session-id>/
|       |-- events_p*.h5
|       |-- session.json
|       |-- partitions.jsonl
|       |-- camera_sync.csv
|       |-- phone.jsonl
|       `-- derived/
`-- queries/
    |-- query_day/
    |   `-- <session-id>/...
    |-- query_dawn/
    |   `-- <session-id>/...
    |-- query_slices.csv
    `-- sessions.txt
```

Download the repository with the Hugging Face CLI by replacing the repository
identifier below:

```bash
hf download <namespace>/<dataset> --repo-type dataset --local-dir springfield
```

## Contents

Each session contains:

| Path | Description |
| --- | --- |
| `events_p*.h5` | Raw event arrays. Database traversals are divided into several partitions; each query pass has one partition. |
| `session.json` | Sensor, capture, timing, distance, and partition metadata. |
| `partitions.jsonl` | Partition boundaries and associated GPS fixes. |
| `camera_sync.csv` | Samples relating the camera clock to the host clock. |
| `phone.jsonl` | Raw phone telemetry. |
| `derived/gps_interp.csv` | Processed GPS samples used by the evaluation. |
| `derived/sync.json` | Fitted camera, phone, and host clock relationships. |
| `derived/*.csv` | Processed location, orientation, and inertial measurements. |

An event HDF5 file contains four equal-length datasets:

| Dataset | Type | Meaning |
| --- | --- | --- |
| `events/x` | `uint16` | Pixel x-coordinate. |
| `events/y` | `uint16` | Pixel y-coordinate. |
| `events/t` | `int64` | Camera timestamp in microseconds. |
| `events/p` | `uint8` | Polarity, encoded as 0 or 1. |

The root HDF5 attributes record the sensor `width`, `height`, and timestamp
scale. Events are raw; no event representation or background-activity filter
has been applied.

### Database viewpoints

The gallery combines all seven sessions:

| Session | Coverage | Camera facing |
| --- | --- | --- |
| `20260829T000024Z` | Full route | Forward |
| `20260829T005503Z` | Full route | Left |
| `20260829T023529Z` | Full route | Right |
| `20260829T032741Z` | Extra location A | Forward |
| `20260829T033229Z` | Extra location A | Right |
| `20260829T033917Z` | Extra location B | Forward |
| `20260829T034314Z` | Extra location B | Right |

### Query selection

`queries/query_slices.csv` is the authoritative query list. The recordings
contain additional raw events, but only the listed windows belong to the
benchmark.

| Column | Meaning |
| --- | --- |
| `session` | Query session identifier. |
| `sweep` | Capture condition: `day` or `dawn`. |
| `slice_index` | Zero-based 50 ms window index within the session event file. |
| `psi_deg` | Signed camera bearing relative to the local route direction. |
| `x_enu_m`, `y_enu_m` | Query position in the shared local ENU coordinate frame. |

| Sweep | Passes | Query windows |
| --- | ---: | ---: |
| Day | 19 | 3,169 |
| Dawn | 19 | 2,388 |

The published subset excludes reversed views using `abs(psi_deg) < 135`.
Consequently, the selected `slice_index` values need not be contiguous.

## Loading a query

Install [EventCV](https://eventcv.net), then open the recording as a lazy 50 ms
reader. The example loads the first selected query without reading the full
HDF5 file:

```bash
pip install eventcv
```

```python
import csv
from pathlib import Path

import eventcv as ecv


root = Path("springfield")
with (root / "queries" / "query_slices.csv").open(newline="") as file:
    row = next(csv.DictReader(file))

session = root / "queries" / f"query_{row['sweep']}" / row["session"]
event_file = next(session.glob("events_p*.h5"))

reader = ecv.open(
    str(event_file),
    dt_ms=50,
    sensor_size=(1280, 720),
    hot_pixel_filter=True,
    hot_pixel_std=3.0,
)
query = reader.slice(int(row["slice_index"]))
events = query.numpy()  # [N, 4] columns: x, y, t, p
frame = query.countmask(white_frame=False).numpy()  # [3, 720, 1280]

print(events.shape, frame.shape)
```

Set `hot_pixel_filter=False` to retrieve the recorded events without the
reference evaluation's hot-pixel filtering. EventCV can render other
representations from the same `query` stream.

## Evaluation protocol

1. Divide every database partition into consecutive 50 ms windows beginning at
   its first event timestamp. Discard windows without valid synchronised GPS.
2. Combine the remaining database windows into one gallery. This gives 132,569
   gallery entries at the native sampling density.
3. Evaluate the 5,557 windows listed in `queries/query_slices.csv`.
4. Treat a retrieved gallery entry as correct when its interpolated GPS position
   is within 25 m of the query position.
5. Report Recall@1, Recall@5, Recall@10, and Recall@20.

The reference evaluation renders raw events with 50 ms accumulation, applies
the eventcv hot-pixel filter with standard-deviation threshold 3, and leaves the
background-activity filter disabled. Other event representations can be built
from the same raw recordings.

## Citation

If you use Springfield-Event, please cite the associated paper and dataset.

```bibtex
@misc{hines2026megaevent,
      title={Multi-viewpoint Geo-localization with Event Cameras}, 
      author={Adam D. Hines and Michael Milford and Tobias Fischer},
      year={2026},
      eprint={2609.21219},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2609.21219}, 
}
```