--- 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](https://arxiv.org/abs/XXXX.XXXXX) Training code (GitHub, TODO): [https://github.com/louisgeist/MALiBU3D](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](https://geoservices.ign.fr/lidarhd) programme and [IGNF/FLAIR-HUB](https://huggingface.co/datasets/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 ```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 # 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, 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: ```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 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 | ```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 TODO: licence to be defined (see `LICENSE`).