| --- |
| license: cc-by-4.0 |
| library_name: numpy |
| tags: |
| - earth-observation |
| - cryosphere |
| - glaciers |
| - sentinel-2 |
| - sentinel-1 |
| - dem |
| - geospatial |
| - numpy |
| - temporal |
| - foundation-model |
| --- |
| |
| # CryoMesh T=4 temporal NPY - 1% public sample |
|
|
| This repository contains a public **1% sample** of the CryoMesh four-timestamp temporal NPY corpus from `all_temporal_npy`. It is hosted as a Hugging Face model repository per release request, but the contents are data files for inspection, prototyping, and reproducibility checks. |
|
|
| The sample is selected at the grid level so every included grid has all **4 timestamps**. The source directory has 2,371 complete four-timestamp grid samples (9,484 files); this release includes 24 grids (96 files), or 1.012% of complete samples. Total payload size is about 92.74 GB (86.37 GiB). |
|
|
| ## Contents |
|
|
| - `data/*.npy` - temporal raster arrays, one file per grid timestamp. |
| - `index.csv` - one row per `.npy` file with grid id, timestamp index, date, region, exact NPY shape, CRS, affine transform, bounds, centroid, and file size. |
| - `samples.csv` - one row per grid sample with the four timestamp dates and per-timestamp shape summary. |
| - `crs_metadata.csv` - per-file CRS, affine transform, bounds, width, and height metadata recovered from the corresponding source GeoTIFFs. |
| - `region_composition.csv` - source and selected sample counts by region. |
| - `selection_summary.csv` - source directory, selected fraction, and byte counts used for this release. |
|
|
| ## Array Format |
|
|
| Each `.npy` file is a `float32` NumPy array in channels-first order with shape `(10, H, W)`. Grid dimensions vary by location, and a few temporal stacks have different `H`/`W` values across timestamps. Use `index.csv` for the exact per-file shape and geospatial transform. |
|
|
| | Index | Band | Source | |
| |---|---|---| |
| | 0 | Blue | Sentinel-2 | |
| | 1 | Green | Sentinel-2 | |
| | 2 | Red | Sentinel-2 | |
| | 3 | NIR | Sentinel-2 | |
| | 4 | SWIR1 | Sentinel-2 | |
| | 5 | SWIR2 | Sentinel-2 | |
| | 6 | Slope | Copernicus DEM derived layer | |
| | 7 | Elevation | Copernicus DEM | |
| | 8 | VV | Sentinel-1 SAR | |
| | 9 | VH | Sentinel-1 SAR | |
|
|
| Note on SAR polarization: over some polar regions Sentinel-1 acquisitions are single-polarization. In those cases the available polarization may be duplicated into the VV/VH slots to preserve a stable 10-channel schema. |
|
|
| ## Region Composition |
|
|
| | Region | Complete Source Samples | Selected Samples | |
| |---|---:|---:| |
| | antarctic | 821 | 8 | |
| | arctic | 774 | 8 | |
| | hma | 276 | 3 | |
| | north_america | 240 | 2 | |
| | south_america_andes | 117 | 1 | |
| | other | 98 | 1 | |
| | europe_alps | 45 | 1 | |
|
|
| ## Usage |
|
|
| ```python |
| import numpy as np |
| import pandas as pd |
| |
| idx = pd.read_csv("index.csv") |
| sample = idx[idx.grid_id == "grid_12153"].sort_values("timestamp_index") |
| sequence = [np.load(path) for path in sample.filename] |
| print(sample.date.astype(str).tolist()) # ['20210109', '20210204', '20211006', '20211125'] |
| print(sequence[0].shape) # (10, H, W) |
| ``` |
|
|
| For geospatial placement, use the per-file `crs`, `transform_*`, and `bounds_*` columns in `index.csv` or `crs_metadata.csv`. |
|
|
| ## Selection Method |
|
|
| Only grid IDs with exactly four `.npy` timestamps were eligible. The selected count is `ceil(0.01 * 2,371) = 24` complete grid samples. Allocation is proportional by region using largest-remainder apportionment, then deterministic even spacing over numeric grid IDs within each region. |
|
|
| ## Provenance and Attribution |
|
|
| Derived from public Earth-observation sources, 2021: |
|
|
| - Contains modified Copernicus Sentinel-2 and Sentinel-1 data. |
| - Includes Copernicus DEM derived and elevation layers. Copernicus DEM copyright: DLR e.V. / Airbus Defence and Space GmbH. |
|
|
| Curation, co-registration, multi-sensor harmonization, cloud screening, tiling, and NPY packaging are original CryoMesh processing work. |
|
|
| ## License |
|
|
| Released under **CC-BY-4.0**. Please retain the Copernicus attributions above and cite CryoMesh when using this sample. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{cryomesh_t4_1pct_sample_2026, |
| title = {CryoMesh T=4 temporal NPY - 1% public sample}, |
| author = {Kaushik, Saurabh and collaborators}, |
| year = {2026}, |
| note = {One percent public sample of the CryoMesh four-timestamp temporal NPY corpus} |
| } |
| ``` |
|
|
