Datasets:

LouisGeist
/

MALiBU3D

license: other
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

A Multitask Aerial LiDAR Benchmark for Large-Scale 3D Scene Understanding.

Paper (arXiv, TODO): https://arxiv.org/abs/XXXX.XXXXX

Training code (GitHub, TODO): https://github.com/louisgeist/MALiBU3D

MALiBU3D is a large-scale multitask ALS benchmark: 59 billion LiDAR points over 2,221 km² of metropolitan France (overlap of the national LiDAR HD programme and IGNF/FLAIR-HUB). It spans urban, agricultural, forested, mountainous, and coastal landscapes, with point-wise intensity and aerial RGB.

Supervision covers five complementary tasks:

land-cover segmentation (15 classes)
forest-cover segmentation
natural-habitat distribution (four ecological axes)
road-network prediction
elevation regression

Zones are ~1 km² and split into 100 × 100 m tiles. Train / val / test follow the FLAIR-HUB departmental split.

This Hub repo ships the training-ready arrays (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.

Download

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

labels.json
palettes.json
scene_split_manifest.csv     # 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):

{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, if present
{tile_id}/natural_habitat.npy       uint8   (N, 4)   ecological axes, if present
{tile_id}/forest_2d.npy             uint8   (1, H, W)
{deptcode}_{roi}_ROADS_graph.gpkg    EPSG:2154, if the ROI has roads

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

Not every tile has a DTM or CarHab coverage, and not every ROI has roads. elevation.npy is omitted where DEM_ELEV is false; natural_habitat.npy where NATURAL_HABITAT is false; the GeoPackage where ROADS is false. Those flags come from scene_split_manifest.csv (the Pointcept split table). tiles.csv repeats them as has_elevation, has_natural_habitat, and has_roads_graph.

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 Pointcept split table (identifiers plus modality flags LIDARHD, NATURAL_HABITAT, DEM_ELEV, ROADS, …). Column patch_id is the 100 m tile. It still lists FLAIR-HUB rows without LiDAR. Reconstruct on disk:

{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 to the 4 ecological axes. 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`	Humid, Mesic, Dry	3
2	`nathab_soil_chemistry`	Acidic, Alkaline	2
3	`nathab_bioclimatic_zone`	Temperate, Mediterranean, Alpine	3

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.

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

TODO: licence to be defined (see LICENSE).