Add README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: etalab-2.0
+license: other
 pretty_name: MALiBU3D
 task_categories:
   - image-segmentation
@@ -19,18 +19,33 @@ configs:
 
 # 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.
+**A Multitask Aerial LiDAR Benchmark for Large-Scale 3D Scene Understanding.**
 
-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.
+Paper (arXiv, TODO): [https://arxiv.org/abs/XXXX.XXXXX](https://arxiv.org/abs/XXXX.XXXXX)
 
-A **tile** is a ~100×100 m square. An **ROI** groups neighbouring tiles (and
-holds the optional road graph).
+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
 
@@ -56,7 +71,7 @@ A tiny extracted ROI lives under `toy/` for inspection.
 ```text
 labels.json
 palettes.json
-scene_split_manifest.csv     # original split table (all FLAIR-HUB rows)
+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
@@ -72,14 +87,22 @@ Each `{roi}.zip` (flat, no wrapping `{roi}/` folder):
 {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}/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    optional, EPSG:2154
+{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
@@ -87,9 +110,10 @@ 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:
+`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}
@@ -107,12 +131,12 @@ global (`labels.json`).
 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`:
+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` | Humide, Mesique, Sec | 3 |
+| 1 | `nathab_moisture_regime` | Humid, Mesic, Dry | 3 |
 | 2 | `nathab_soil_chemistry` | Acidic, Alkaline | 2 |
 | 3 | `nathab_bioclimatic_zone` | Temperate, Mediterranean, Alpine | 3 |
 
@@ -152,11 +176,4 @@ 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.
+TODO: licence to be defined (see `LICENSE`).