---
license: cc-by-4.0
pretty_name: HiPredict dataset
configs:
- config_name: preview
  default: true
  data_files:
  - split: preview
    path: preview/*
size_categories:
- 100K<n<1M
tags:
- hirise
- mars
- planetary-science
- image-reconstruction
- numpy
---
# HiPredict dataset

**Paper:** preprint coming soon · **Code:** [ardan-S/HiPredict](https://github.com/ardan-S/HiPredict) · **Model:** [ardan-S/HiPredict](https://huggingface.co/ardan-S/HiPredict)

The patches and records used to train and evaluate HiPredict. HiPredict
reconstructs the RED4 band of the HiRISE camera on the Mars Reconnaissance
Orbiter. The RED4 detector has partially failed, and many observations have no
usable RED4 band as a result.

## Contents

| Record and arrays | Patch sets | Observations | Arrays | Observation dates |
| --- | ---: | ---: | ---: | --- |
| `data_record_alls.csv`, `npy_files/` | 116,790 | 19,465 | 583,950 | 2008-12-21 to 2021-08-08 |
| `data_record_evals.csv`, `npy_files_evals/` | 500 | 500 | 2,500 | 2007-09-30 to 2023-05-27 |

The arrays are stored in 51 uncompressed tar shards (`shards/0000/part-*.tar`,
about 216 GB in total). `dataset_manifest.json` lists each shard's SHA-256
checksum and the provenance of the two records.

Each patch set holds five float32 arrays from one observation:

- BG12, IR10 and RED4: 256 × 256 patches of the same ground;
- RED3 and RED5: 1024 × 32 × 4 context arrays from the detector edges next to RED4.

Values are in the calibrated units of the processed ISIS cubes.

## Preview

The dataset viewer shows `preview/`: false-colour composites of 100 patch sets,
spread evenly over the record's dates, with IR10, RED4 and BG12 as red, green
and blue, each contrast-stretched independently. They are for browsing only;
the data are the float32 arrays in the shards.

## Records

Each record has one row per array. The columns are:

- `Observation`, `Set`, `Patch` and `CCD`: which observation, patch set and detector the array belongs to;
- `Path`: the array's location, relative to the CSV file;
- `Height`, `Width` and `Channels`: the array's shape;
- `Patch_x` and `Patch_y`: where the patch sits in the processed cube;
- `Date`, `Binning` and `TDI`: observation settings;
- observation metadata from the HiRISE EDR index, including instrument temperatures, viewing and illumination geometry, orbit number, and solar longitude and distance;
- `Pix_min`, `Pix_max`, `Obs_center` and `Obs_scale`: pixel range and observation-level centre and scale.

## Splits

There are no split files. The code splits `data_record_alls.csv` by date: patch
sets are sorted by observation date, and the earliest 70% are used for training,
the next 10% for validation and the last 20% for testing. On this record that
gives 81,753, 11,679 and 23,358 sets, with boundaries on 2018-02-10 and
2018-11-19. The released model was trained when the record held 51 fewer patch
sets, so its training split had 81,717 sets.

`data_record_evals.csv` is the evaluation set for the per-category metrics
defined in the code's `hipredict/configs/evals.yaml`. 314 of its 500
observations also appear in `data_record_alls.csv` with identical patches, 240
of them in the training split.

## Processing

Observations were selected from the HiRISE archive in the NASA Planetary Data
System. Each one has all five detectors used here (RED3, RED4, RED5, IR10 and
BG12), binning 1 or 2 and TDI 64 or 128, with matching binning across the RED
detectors and across the colour detectors. They were processed with HiPROC
0.11.0 and ISIS 9.0.0 using the pipeline in the code repository. Patch positions
were sampled to spread the patch sets over location, orbit, solar longitude and
binning/TDI.

## Download and use

To use the dataset with the HiPredict code, download it into the repository's
`files/` folder and unpack the shards there:

```bash
uv run hf download ardan-S/HiPredict --repo-type dataset --local-dir files \
  --exclude README.md --exclude .gitattributes
for shard in files/shards/*/*.tar; do tar -xf "$shard" -C files; done
```

Unpacking needs as much space again as the download. See the code's
[workflows guide](https://github.com/ardan-S/HiPredict/blob/main/docs/workflows.md#the-hipredict-dataset)
for storing the dataset on another disk and for training and evaluating with it.

The records can also be used directly:

```python
import numpy as np
import pandas as pd

record = pd.read_csv("files/data_record_alls.csv")
row = record.iloc[0]
array = np.load(f"files/{row.Path}")
```

## Licence and credit

The dataset is released under the Creative Commons Attribution 4.0 licence.
HiRISE data: NASA/JPL/University of Arizona.

## Citation

A citation will be added here when the preprint is released.