| --- |
| 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. |
|
|
