---
license: etalab-2.0
pretty_name: MALiBU3D
task_categories:
  - image-segmentation
size_categories:
  - n>1T
tags:
  - point-cloud
  - lidar
  - remote-sensing
  - semantic-segmentation
  - france
  - ign
configs:
  - config_name: catalog
    data_files: tiles.parquet
---

# MALiBU3D

Large-scale **training-ready** airborne LiDAR point clouds over France, with
land-cover labels, natural-habitat axes, canopy-height (`elevation`), RGB, and
road-network graphs. Built on
[IGNF/FLAIR-HUB](https://huggingface.co/datasets/IGNF/FLAIR-HUB) 100 m tiles
plus IGN LiDAR HD. Former working name: Flair3D.

This Hub repo ships the **preprocessed** arrays used for training (NumPy `.npy`
inside one **zip per ROI**), not the raw GeoTIFFs / PLYs. Unzip onto local
scratch for training (`np.load` / mmap). The zip is the distribution format.

A **tile** is a ~100×100 m square. An **ROI** groups neighbouring tiles (and
holds the optional road graph).

## Download

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="ORG/MALiBU3D",
    repo_type="dataset",
    allow_patterns=["data/train/D075-2021_LIDARHD/*", "labels.json", "tiles.csv"],
)
```

Do **not** `load_dataset()` on the point clouds (variable `N` ≈ 1e5–3e5). The
Dataset Viewer reads `tiles.parquet` (one row per tile, no xyz). `tiles.csv`
is the same table when parquet was not built. Tiles with missing LiDAR
coordinates are omitted from the catalog.

A tiny extracted ROI lives under `toy/` for inspection.

## Layout

```text
labels.json
palettes.json
scene_split_manifest.csv     # original split table (all FLAIR-HUB rows)
tiles.parquet                # Hub viewer catalog (released tiles only)
tiles.csv                    # same catalog (zip_path, forest georef, n_points, …)
SHA256SUMS
toy/                          # one extracted ROI
data/{train,val,test}/{dept}_LIDARHD/{roi}.zip
```

Each `{roi}.zip` (flat, no wrapping `{roi}/` folder):

```text
{tile_id}/coord.npy                 float32 (N, 3)   XYZ relative
{tile_id}/coord_translation.npy     float64 (3,)     Lambert-93 offset
{tile_id}/color.npy                 uint8   (N, 3)
{tile_id}/segment.npy               uint8   (N,)     land cover, Void=15
{tile_id}/strength.npy              float32 (N,)     LiDAR intensity ~[0, 1]
{tile_id}/elevation.npy             float32 (N,)     z − DTM (optional)
{tile_id}/natural_habitat.npy       uint8   (N, 4)   ecological axes (optional)
{tile_id}/forest_2d.npy             uint8   (1, H, W)
{deptcode}_{roi}_ROADS_graph.gpkg    optional, EPSG:2154
```

Absolute coordinates: `xyz_abs = coord + coord_translation` (EPSG:2154).

**Not included:** per-tile `meta.json`, `network.npy`, per-point `forest.npy`,
`land_use.npy`, rail / transmission-line graphs. `RAILROADS` /
`TRANSMISSION_LINES` columns in the catalog remain as FLAIR-HUB availability
flags.

## Manifest vs catalog

`scene_split_manifest.csv` is the **original** split table (identifiers only,
column `patch_id` = the 100 m tile). It still lists FLAIR-HUB rows without
LiDAR. Reconstruct on disk:

```text
{data_root}/{split}/{dept_year}_LIDARHD/{roi}/{tile_id}
```

`tiles.csv` / `tiles.parquet` is the **release catalog**: only tiles shipped
here (`tile_id` = that same identifier), plus a relative `zip_path`
(`data/train/D075-2021_LIDARHD/UU-S1-4.zip`), `n_points`, and `forest_2d`
georeferencing (`forest_origin_x`, `forest_origin_y`, `forest_width`,
`forest_height`, `forest_pixel_m`). CRS, south-up axis, and class values are
global (`labels.json`).

## Labels

See `labels.json`. Land cover (`segment.npy`): 15 train classes + Void=15.

Natural habitat (`natural_habitat.npy`): **`(N, 4)` uint8**, already remapped
from CarHab. Column order is `labels.json` → `natural_habitat.columns`:

| col | key | classes | Void |
| --- | --- | --- | --- |
| 0 | `nathab_habitat_type` | Open, Forest, Mineral, Aquatic | 4 |
| 1 | `nathab_moisture_regime` | Humide, Mesique, Sec | 3 |
| 2 | `nathab_soil_chemistry` | Acidic, Alkaline | 2 |
| 3 | `nathab_bioclimatic_zone` | Temperate, Mediterranean, Alpine | 3 |

```python
import json, numpy as np
labels = json.load(open("labels.json"))
nh = np.load("natural_habitat.npy")          # (N, 4)
moisture = nh[:, 1]                          # Void = 3
# Pointcept: no LUT. Assign column i to task labels["natural_habitat"]["columns"][i].
```

## `forest_2d` → points

South-up Lambert-93 grid. **Always read `forest_pixel_m` / origin from the
catalog** (do not assume 0.5 m). Width/height also vary slightly per tile.

```python
import numpy as np
import pandas as pd

row = pd.read_csv("tiles.csv").set_index("tile_id").loc[tile_id]
coord = np.load("coord.npy")
t = np.load("coord_translation.npy")
raster = np.load("forest_2d.npy")  # (1, H, W), 0 / 1 / 2=void
x = coord[:, 0] + t[0]
y = coord[:, 1] + t[1]
ix = np.floor((x - row.forest_origin_x) / row.forest_pixel_m).astype(int)
iy = np.floor((y - row.forest_origin_y) / row.forest_pixel_m).astype(int)
h, w = int(row.forest_height), int(row.forest_width)
inside = (ix >= 0) & (ix < w) & (iy >= 0) & (iy < h)
out = np.full(len(coord), 2, dtype=np.uint8)
out[inside] = raster[0, iy[inside], ix[inside]]
```

Road graphs: GeoPackage layers `nodes`, `edges`, `metadata`, coordinates
absolute EPSG:2154.

## Licence and attribution

Licence Ouverte 2.0 / Etalab (see `LICENSE`). Attribute IGN and FLAIR-HUB.
Derived semantic labels and graphs are produced by this project.

## Loader notes

On-disk `segment` is **uint8** `(N,)`. `natural_habitat` is **uint8** `(N, 4)`.
Cast if a loader asserts another integer dtype. Per-point `forest.npy`,
`land_use.npy`, and `network.npy` are absent.