TreeUQ — Geographically-Tiled Bavaria EO Benchmark

TreeUQ is a large-scale Earth observation benchmark for tree species mapping and tree structure estimation (height, count, density, variance) over Bavaria, Germany.

Each 128×128 pixel patch (10 m resolution, EPSG:25832) contains:

• Sentinel-2 — 4 seasonal composites (spring/summer/autumn/winter 2025), 10 bands
• Sentinel-1 GRD — 4 seasonal composites, VV + VH polarisation, linear gamma-0
• Tree species raster — Bavarian species classification aligned to the 10 m grid
• Supervision labels — per-pixel height, count, density from the Bavarian Einzelbäume individual-tree inventory
• DOP20 aerial imagery (optional) — 20 cm RGB orthophotos as 6400×6400 chips

Splits (spatial blocks): train 31,806 | validation 7,048 | test 6,565 (45,419 Bavaria patches total).

The source grid is a rectangle around Bavaria: 79,943 patches, of which 45,419 have in_bavaria=True and 34,524 fall outside the state. Outside patches have no inventory labels and are not in this release. Training and evaluation use only in_bavaria=True.

The dataset viewer reports 90,838 rows because it also loads data/index/*_split_seed42.parquet. Those three files are a superseded alternate split of the same 45,419 patches (every patch_id matches; 20,674 of them have a different split label). Use only train.parquet, validation.parquet, and test.parquet.

Croissant 1.0 metadata: croissant.json

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Selective Download — Get Only What You Need

This dataset is 5+ TB in total. You do not need to download it all. The layout is organised into 10 km × 10 km geographic tiles with modalities separated:

File	Size per tile	Contents
data/tiles/<tile>/core.tar	~10–50 MB	S2 + S1 + species + labels
data/tiles/<tile>/dop20.tar	~350 MB	DOP20 20 cm RGB (optional)
data/index/<split>.parquet	~1 MB total	Patch metadata + tile mapping

Minimum download for a 10 km region: ~10–50 MB (core only, no DOP20).

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Quick Start

1. Install dependencies

pip install huggingface_hub pandas pyarrow numpy

2. Filter by bounding box and download tiles

from huggingface_hub import hf_hub_download
import pandas as pd

REPO = "iclr2027kiwi/TreeUQ"

# Step 1 — download only the index (~1 MB, instant)
idx_path = hf_hub_download(REPO, "data/index/train.parquet", repo_type="dataset")
idx = pd.read_parquet(idx_path)

# Step 2 — filter by bounding box in EPSG:25832 (metres)
# Example: ~20 km around Munich (approximate)
bbox = (680_000, 5_325_000, 702_000, 5_345_000)  # minx, miny, maxx, maxy
roi = idx[
    (idx["center_x"] >= bbox[0]) & (idx["center_x"] <= bbox[2]) &
    (idx["center_y"] >= bbox[1]) & (idx["center_y"] <= bbox[3])
]
print(f"{len(roi)} patches across {roi['geographic_tile'].nunique()} tiles")

# Step 3 — download only the tiles you need
local_tars = []
for tile in roi["geographic_tile"].unique():
    path = hf_hub_download(
        REPO, f"data/tiles/{tile}/core.tar", repo_type="dataset"
    )
    local_tars.append(path)
    print(f"  {tile}: downloaded")

3. Decode tensors from a core.tar

import tarfile, json
import numpy as np

# Load the schema once
schema_path = hf_hub_download(REPO, "data/schema.json", repo_type="dataset")
with open(schema_path) as f:
    schema = json.load(f)
core_members = schema["core_members"]

patch_ids_wanted = set(roi["patch_id"].tolist())

for tar_path in local_tars:
    with tarfile.open(tar_path, "r:") as tf:
        # Build a {key: {suffix: TarInfo}} lookup
        by_key = {}
        for m in tf.getmembers():
            if m.isfile() and "." in m.name:
                key, suffix = m.name.split(".", 1)
                by_key.setdefault(key, {})[suffix] = m

        for key, members in by_key.items():
            patch_id = int(key)
            if patch_id not in patch_ids_wanted:
                continue

            # Decode each tensor
            arrays = {}
            for suffix, spec in core_members.items():
                if suffix in members:
                    raw = tf.extractfile(members[suffix]).read()
                    arrays[suffix] = np.frombuffer(
                        raw, dtype=np.dtype(spec["dtype"])
                    ).reshape(spec["shape"])

            s2 = arrays["s2_autumn.f32"]   # (128, 128, 10) float32  DN
            s2_reflectance = s2 / 10_000   # normalise to [0, 1]
            labels = arrays["tree_count.f32"]  # (128, 128) float32

            print(f"patch {patch_id}: S2 shape={s2.shape}, labels shape={labels.shape}")
            break

4. Download DOP20 aerial imagery (optional)

# Only download DOP20 for tiles where it is available
for tile in roi[roi["dop20_available"]]["geographic_tile"].unique():
    dop20_path = hf_hub_download(
        REPO, f"data/tiles/{tile}/dop20.tar", repo_type="dataset"
    )
    print(f"DOP20 {tile}: {dop20_path}")

# DOP20 member key inside dop20.tar: {patch_id:06d}.dop20_rgb.u8
# Shape: (6400, 6400, 3)  dtype: uint8

The steps above are the supported download path. This dataset repository does not include a separate download script.

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Repository Layout

data/
  index/
    train.parquet          ← 31,806 patches (canonical train split)
    validation.parquet     ← 7,048 patches (column split = val)
    test.parquet           ← 6,565 patches
    *_split_seed42.parquet ← superseded alternate split of the same patches; ignore
  schema.json              ← tensor member definitions (dtype, shape, band names)
  tiles/
    tile_<minx_km>_<miny_km>/    ← one dir per 10 km × 10 km grid cell (EPSG:25832)
      core.tar             ← all S2/S1/label/species members for patches in this tile
      dop20.tar            ← DOP20 RGB chips (only where dop20_available=True)
      manifest.json        ← patch_ids, bbox, split counts
croissant.json

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Index Parquet Schema (data/index/*.parquet)

Column	Type	Description
patch_id	int64	Unique patch ID on the Bavaria 10 m grid
center_x, center_y	float64	Patch centre in EPSG:25832 (metres)
row_start, row_end, col_start, col_end	int64	Extent in the global grid
valid_pixel_pct	float64	Fraction of non-NaN Sentinel-2 pixels
tree_pixel_pct	float64	Fraction of pixels with ≥1 inventory tree
mean_tree_count	float64	Mean tree count across the 128×128 pixels
mean_tree_count_variance	float64	Mean spatial tree-count variance across the patch
split	string	train / val / test (validation file uses val)
block_id, block_row, block_col	int64	Spatial block (6×6 grid) for split assignment
distance_to_nearest_test_km	float64	Distance from the patch centre to the nearest test patch centre
buffered	bool	Whether the patch lies in the 5 km buffer around test blocks
in_bavaria	bool	Whether centre lies inside Bavaria boundary
dop20_coverage_pct	float64	Fraction of the patch with DOP20 pixels
dop20_available	bool	True when dop20_coverage_pct ≥ 0.5
geographic_tile	string	Tile directory name, e.g. tile_680_5330
sample_key	string	Zero-padded patch_id, prefix for tar members
tile_relpath	string	Repo-relative path to core.tar
dop20_relpath	string	Repo-relative path to dop20.tar

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Tensor Schema (data/schema.json)

core_members — in core.tar

Member suffix	Shape	dtype	Description
s2_spring.f32	(128,128,10)	float32	Sentinel-2 spring 2025; bands B2 B3 B4 B8 B5 B6 B7 B8A B11 B12
s2_summer.f32	(128,128,10)	float32	Sentinel-2 summer 2025
s2_autumn.f32	(128,128,10)	float32	Sentinel-2 autumn 2025
s2_winter.f32	(128,128,10)	float32	Sentinel-2 winter 2025–26
s1_spring.f32	(128,128,2)	float32	Sentinel-1 VV+VH spring 2025 (linear gamma-0)
s1_summer.f32	(128,128,2)	float32	Sentinel-1 VV+VH summer 2025
s1_autumn.f32	(128,128,2)	float32	Sentinel-1 VV+VH autumn 2025
s1_winter.f32	(128,128,2)	float32	Sentinel-1 VV+VH winter 2025–26
tree_species.u8	(128,128)	uint8	Species class IDs (0–7 = species, 0 = Beech; 11 = background / no-data)
mean_height.f32	(128,128)	float32	Mean tree height per cell (m); NaN where no trees
median_height.f32	(128,128)	float32	Median tree height per cell (m)
height_variance.f32	(128,128)	float32	Height variance per cell
tree_count.f32	(128,128)	float32	Inventory trees per 10 m cell
tree_density.f32	(128,128)	float32	3×3 neighbourhood tree count sum
tree_count_variance.f32	(128,128)	float32	Spatial variance of tree count
{key}.json	—	JSON	Patch metadata sidecar

tree_species class IDs: 0 Beech, 1 Douglas fir, 2 Fir, 3 Larch, 4 Oak, 5 Other deciduous, 6 Pine, 7 Spruce, 11 background / no-data. Class 0 is Beech, not nodata.

dop20_members — in dop20.tar

Member suffix	Shape	dtype	Description
dop20_rgb.u8	(6400,6400,3)	uint8	20 cm RGB orthophoto; bands R G B

Normalisation: Divide S2 DN by 10,000 to get approximate [0, 1] surface reflectance. S1 values are linear gamma-0 (not dB); typical range 0–1; log-transform before model input.

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Spatial Split

Patches are assigned to splits using a 6×6 geographic block grid (~61×60 km per block). Test and validation blocks are chosen to cover distinct rows and columns so hold-outs are spread across Bavaria (not a random shuffle). Entire blocks are held out to prevent spatial autocorrelation leakage. Minimum train→test centroid distance: 30.1 km. The Parquet indexes are the source of truth for split; JSON sidecars inside existing tar files may still carry the previous label.

Split	Patches	With trees	Mean tree_pixel_pct
train	31,806	30,991 (97.4 %)	30.7 %
val	7,048	6,710 (95.2 %)	31.6 %
test	6,565	6,215 (94.7 %)	35.2 %

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Limitations

• Geography: Labels cover inventoried urban and park trees; most Bavarian forest carries no label signal.
• Sparsity: Only ~1.2 % of pixels have tree labels; use masked losses or foreground-biased sampling.
• Boundary effects: The AOI is a rectangle; 43 % of grid patches lie outside Bavaria. All patches in this release are pre-filtered to in_bavaria=True.
• Generalisation: Models trained on this dataset may not transfer outside Bavaria without domain adaptation.