| --- |
| 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. |
|
|
