Datasets:

ardan-S
/

HiPredict

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 · Model: 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.

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:

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 for storing the dataset on another disk and for training and evaluating with it.

The records can also be used directly:

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.