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ESA-philab/OceanDepths

OceanDepths GeoTIFF Raster and Aligned ARGO Dataset This dataset package contains the model-ready Ocean variables (ARGO submarine data, sea surface height, sea surface temperature and salinity, as well as GLORYS reanalysis information for 50 depth levels. The ARGO data has been projected onto the GLORYS grid in order to build a ML-ready dataset. The intention is that users can create tensors easily for CV-inspired ML approaches to ocean-variable reconstruction. While… See the full description on the dataset page: https://huggingface.co/datasets/ESA-philab/OceanDepths.

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Dataset Card

OceanDepths GeoTIFF Raster and Aligned ARGO Dataset

This dataset package contains the model-ready Ocean variables (ARGO submarine data, sea surface height, sea surface temperature and salinity, as well as GLORYS reanalysis information for 50 depth levels. The ARGO data has been projected onto the GLORYS grid in order to build a ML-ready dataset. The intention is that users can create tensors easily for CV-inspired ML approaches to ocean-variable reconstruction. While traditionally this problem has been approached point-wise, this dataset enables easy training of CV models such as inpainitng-inspired approaches.

Dataset Preview

Rasterized Argo-profiles aligned with GLORYS product, examples:

Related Dataset

OceanTACO is a concurrent development of a related global ocean dataset built from overlapping source products. We focus on L4 products and make some assumptions for the users in order to enable comparative benchmarks. If you want easy access to the actual underlying data, check out this dataset.

Layout

The rasters/ directory is intentionally at the repository root. It contains the aligned uint8 GeoTIFF products used by the pixel-space dataloader. The compact argo/argo_profiles_on_grid.zarr store is the grid-indexed ARGO input used by that dataloader.

The package intentionally contains two ARGO Zarr stores with different roles. argo/argo_profiles_on_grid.zarr is the compact grid-indexed store meant to be used together with the GeoTIFF raster dataset. data/argo_glors_ostia_ssh.zarr is the full enriched profile-level store and holds the complete ARGO collocation dataset, including the sampled GLORYS, OSTIA, sea-level, and sea-surface-salinity context.

The compact store's public argo_temp_* fields contain EN4 POTM_CORRECTED: ITS-90 sea-water potential temperature referenced to 0 dbar. GLORYS thetao is also potential temperature. The full enriched store retains both argo_potm_on_glorys_depth and the separate in-situ argo_temp_on_glorys_depth field for scientific comparison.

Raster Products

All GeoTIFF rasters are exported on the GLORYS 0.1 degree global grid (EPSG:4326, 3600 x 1800 pixels, west-to-east longitudes from -180 to 180 and north-to-south latitudes from 90 to -90). The current package contains 1283 weekly target dates per raster product, from 2000-01-01 through 2024-07-26. Files are named <variable>_YYYYMMDD.tif.

The GLORYS variables are depth-resolved 50-band GeoTIFFs:

—rasters/glorys/thetao/: potential temperature, encoded as Kelvin.
—rasters/glorys/so/: salinity, encoded as PSU.

The surface products are single-band GeoTIFFs aggregated to the same weekly target dates with a centered 7-day mean window:

—rasters/ostia/analysed_sst/: OSTIA analysed sea-surface temperature in Kelvin.
—rasters/sealevel/adt/: absolute dynamic topography in meters.
—rasters/sss/sos/: sea-surface salinity in PSU.
—rasters/sss/dos/: sea-surface density in kg/m3.

Raster pixels are stored as uint8 with 255 reserved for nodata. Valid codes 0..254 are linearly decoded using the stretch ranges in manifest.yaml; per-file statistics, source filenames, compression, target dates, and the full depth axis are also recorded there.

PyTorch Dataset and DataLoader

The repository includes a standalone loader package in depthdif_dataset/. It is designed to work directly from a local Hugging Face dataset checkout without installing the full OceanDepths training repository.

Install the loader dependencies in your environment:

bash

pip install -r requirements-loader.txt

Minimal PyTorch usage from the dataset repository root:

python

from depthdif_dataset import ArgoGeoTIFFGriddedPatchDataset, build_dataloader

dataset = ArgoGeoTIFFGriddedPatchDataset(
    geotiff_root_dir=".",
    split="all",
    tile_size=128,
    patch_stride=128,
    max_dates=1,
    metadata_cache_dir=None,
)
loader = build_dataloader(dataset, batch_size=2, num_workers=0)
batch = next(iter(loader))
print(batch["x"].shape, batch["eo"].shape, batch["land_mask"].shape)

The default sample contains normalized tensors for sparse ARGO temperature input (x), dense GLORYS temperature target (y), dense surface context (eo), validity masks, the ocean land_mask, target date, patch coordinates, and a small info dictionary. Set include_salinity=True to add x_salinity, y_salinity, and their masks. Use eo_source="sss" with eo_var_name="sos" to use sea-surface salinity as the surface context instead of OSTIA SST.

A runnable smoke test is included:

bash

python examples/torch_dataloader.py --root . --date-start 20000101 --max-dates 1 --batch-size 2

ARGO-support filtering for train/validation splits requires counting profile overlap per patch/date. The loader skips that scan for split="all" unless count_argo_support=True or --require-argo is set in the example script.

Patch Grid, Overlap, and Land Filtering

The loader builds square patches on the fixed 0.1 degree GLORYS grid. With the default tile_size=128, one sample covers a 12.8 x 12.8 degree region and has 50 depth bands. patch_stride controls how far the next patch starts:

—patch_stride=128: non-overlapping global tiles.
—patch_stride=96: 32-pixel overlap, or 3.2 degrees at 0.1 degree resolution.
—patch_stride=32: 96-pixel overlap, or 9.6 degrees at 0.1 degree resolution.

Smaller strides create more samples and make neighboring patches share more context, but they also increase row-index size and training time. If you use overlap for train/validation splits, prefer a temporal validation split such as val_year=2018; spatial random splits with overlapping patches can leak nearly identical context between train and validation.

max_land_fraction filters out patches that are mostly land. The default max_land_fraction=0.30 keeps patches with at least 70 percent ocean pixels. Increase it for coastal finetuning, or decrease it for open-ocean training. The returned land_mask tensor uses 1 for ocean/support pixels and 0 for land or unavailable support.

Common Loader Recipes

Use a small date slice for quick inspection without building a large metadata cache:

python

dataset = ArgoGeoTIFFGriddedPatchDataset(
    geotiff_root_dir=".",
    split="all",
    date_start=20000101,
    max_dates=1,
    tile_size=128,
    patch_stride=128,
    metadata_cache_dir=None,
)

Use overlapping patches for training:

python

dataset = ArgoGeoTIFFGriddedPatchDataset(
    geotiff_root_dir=".",
    split="train",
    tile_size=128,
    patch_stride=32,
    val_year=2018,
    metadata_cache_dir="depthdif_cache",
)

Use salinity targets and ARGO salinity inputs:

python

dataset = ArgoGeoTIFFGriddedPatchDataset(
    geotiff_root_dir=".",
    split="all",
    include_salinity=True,
    output_fields=("temperature", "salinity"),
    metadata_cache_dir=None,
    max_dates=1,
)

Use sea-surface salinity as the surface context instead of OSTIA SST:

python

dataset = ArgoGeoTIFFGriddedPatchDataset(
    geotiff_root_dir=".",
    split="all",
    eo_source="sss",
    eo_var_name="sos",
    include_salinity=True,
    metadata_cache_dir=None,
    max_dates=1,
)

Use synthetic sparse observations sampled from the dense GLORYS target instead of real ARGO profiles:

python

dataset = ArgoGeoTIFFGriddedPatchDataset(
    geotiff_root_dir=".",
    split="all",
    synthetic_mode=True,
    synthetic_pixel_count=250,
    require_argo_for_all=False,
    metadata_cache_dir=None,
    max_dates=1,
)

Filter samples to patch/date rows that contain ARGO support. This does extra index work and is worth caching when used repeatedly:

python

dataset = ArgoGeoTIFFGriddedPatchDataset(
    geotiff_root_dir=".",
    split="all",
    count_argo_support=True,
    require_argo_for_all=True,
    metadata_cache_dir="depthdif_cache",
    date_start=20000101,
    max_dates=1,
)

The ARGO support map below shows why this filter changes the row distribution: many open-ocean patches have dense support, while other valid ocean patches may have no profiles for a given weekly target date.

Sample Dictionary

The default dataset returns normalized tensors:

—x: sparse ARGO temperature input, shape (50, tile_size, tile_size).
—y: dense GLORYS temperature target, shape (50, tile_size, tile_size).
—eo: dense surface context, shape (1, tile_size, tile_size).
—x_valid_mask and y_valid_mask: boolean temperature masks.
—x_valid_mask_1d: depth-collapsed sparse-input support mask.
—land_mask: ocean/support mask, shape (1, tile_size, tile_size).
—date: target date as YYYYMMDD.
—coords: patch center latitude and longitude when return_coords=True.
—info: patch/date metadata when return_info=True.

When include_salinity=True, samples also include x_salinity, y_salinity, x_salinity_valid_mask, y_salinity_valid_mask, and x_salinity_valid_mask_1d.

Temperatures are normalized from Celsius using the OceanDepths training statistics; salinity is normalized from PSU. To recover physical units:

python

from depthdif_dataset import salinity_normalize, temperature_normalize

temperature_c = temperature_normalize(mode="denorm", tensor=batch["y"])
salinity_psu = salinity_normalize(mode="denorm", tensor=batch["y_salinity"])

ARGO Alignment Examples

ARGO profiles are projected onto the fixed 50-level GLORYS depth axis before spatial rasterization. The compact ARGO-derived temperature is EN4 POTM_CORRECTED potential temperature at 0 dbar, matching the physical temperature convention of GLORYS thetao. The examples below show the grid-indexed ARGO representation and profile-level alignment quality.

The full enriched profile-level ARGO collocation dataset is available at:

python

import xarray as xr

ds = xr.open_zarr("data/argo_glors_ostia_ssh.zarr", consolidated=None)

The lightweight Parquet indices are included for preview and filtering:

python

import pandas as pd

profiles = pd.read_parquet("indices/profiles.parquet")
variables = pd.read_parquet("indices/variables.parquet")

Coverage:

—Raster target dates: 2000-01-01 to 2024-07-26
—Raster target date count per product: 1283
—Enriched ARGO profiles: 9485977
—Enriched ARGO profile dates: 2000-01-01 to 2024-07-31
—Compact grid-indexed ARGO profiles: 9451644
—GLORYS depth levels: 50

License

The package is released as CC BY 4.0. Upstream product licenses and citation requirements for EN4/ARGO, GLORYS, OSTIA, sea-level, and sea-surface-salinity products still apply; see LICENSE for the attribution notice.

Copyright

2026, Simon Donike, Ruben Cartuyvels