Update README.md

README.md

@@ -59,7 +59,7 @@ dataset_info:
 ## Dataset Summary
 - Generation code and pipeline: https://github.com/Anfera/HHDC-Creator (HHDC-Creator repo).
 - 3-D photon-count waveforms (Hyperheight data cubes) built from NEON discrete-return LiDAR using the HHDC pipeline (`hhdc/cube_generator.py`).
-- Each cube stores a high-resolution canopy volume (default: 0.5 m vertical bins over 64 m height, footprints every 2 m) across a 96 m × 96 m tile; actual settings are recorded per-sample in `metadata`.
+- Each cube stores a high-resolution canopy volume (default: 0.5 m vertical bins over 64 m height, footprints every 2 m) across a 96 m × 96 m tile. In the HHDC-Creator pipeline, the exact settings are recorded per-sample in metadata, but this HF dataset only exposes the processed cubes and filenames.
 - Inputs for learning are simulated observations from the physics-based forward imaging model (`hhdc/forward_model.py`) that emulates the Concurrent Artificially-intelligent Spectrometry and Adaptive Lidar System (CASALS), applying Gaussian beam aggregation, distance-based photon loss, and mixed Poisson + Gaussian noise to downsample/perturb the cube.
 - Targets are the clean, high-resolution cubes. The pairing supports denoising and spatial super-resolution with recommended settings of 10 m diameter footprints sampled on a 3 m × 6 m grid (along/across swath); users can adjust these parameters as needed.
 
@@ -69,16 +69,40 @@ dataset_info:
 - Robust reconstruction under realistic sensor noise simulated by the forward model.
 
 ## Dataset Structure
-- **File layout:** NPZ archives, one per tile. Recommended HF repo structure: `train/`, `val/`, `test/` folders containing NPZs (or store them directly with split metadata in the HF dataset script).
-- **Per-sample contents (npz keys):**
-  - `clean_cube`: `int32`, shape `[bins, H, W]` — high-res histogram (after swapping to channel-first).
-  - `x_centers`, `y_centers`: `float64` — footprint centers (meters, projected CRS from NEON tiles).
-  - `bin_edges`: `float64` — height bin edges (meters).
-  - `footprint_counts`: `int32` — raw point counts per footprint before histogramming.
-  - `metadata`: JSON string with cube config, tile bounds, tile indices, outlier quantile used, altitude, and source file name.
-- **Typical shapes (example with `cube_config_sample.json`, `cube_length=96 m`, and `footprint_separation=2 m`):**
-  - `clean_cube`: `[128, 48, 48]` (64 m / 0.5 m vertical bins; 96 m tile / 2 m footprint grid).
-  - `noisy_cube`: `[128, 32, 16]` when using `output_res_m=(3.0, 6.0)` in the forward model.
+
+### Storage and splits
+
+- **Format on the Hub:** Apache Arrow / Parquet, managed by 🤗 Datasets.
+- **Access:** via `load_dataset("anfera236/HHDC", split=...)`.
+- **Splits:** `train`, `validation`, `test` (see `dataset_info` for exact sizes).
+
+### Per-sample fields
+
+Each sample in this Hugging Face dataset contains:
+
+- **`cube`** — `float32`, shape `[128, 48, 48]`  
+  High-resolution Hyperheight data cube (channel-first: `[bins, H, W]`), derived from NEON discrete-return LiDAR using the HHDC-Creator pipeline.
+- **`filename`** — `string`  
+  Identifier for the source tile / sample (matches the tile-level naming used in HHDC-Creator).
+
+Additional fields produced by the HHDC-Creator pipeline (e.g. `x_centers`, `y_centers`, `bin_edges`, `footprint_counts`, `metadata`) are **not stored** in this HF dataset. They can be regenerated from NEON AOP LiDAR using the code in the HHDC-Creator repository.
+
+### Typical shapes and forward model
+
+With the default cube configuration (e.g. `cube_config_sample.json`, `cube_length = 96 m`, `footprint_separation = 2 m`):
+
+- **Clean high-res cube (`cube`):** `[128, 48, 48]`  
+  - 64 m vertical extent / 0.5 m bins → 128 height bins  
+  - 96 m × 96 m tile / 2 m grid → 48 × 48 footprints
+
+Low-resolution, noisy measurements are **generated on the fly** using the physics-based forward model (`LidarForwardImagingModel` in HHDC-Creator). For example, with `output_res_m=(3.0, 6.0)`:
+
+- **Noisy cube (model output, not stored in the dataset):** `[128, 32, 16]`
+
+Users are expected to:
+1. Load `cube` from this dataset as the clean target.
+2. Apply the forward model to obtain noisy / low-res inputs for denoising and super-resolution experiments.
+
 
 ## Usage
 ```python
@@ -122,7 +146,7 @@ loss.backward()
 - Recommended metrics: PSNR and SSIM on the canopy height model (CHM), digital terrain model (DTM), and 50th percentile height maps (all derivable via `hhdc.canopy_plots.create_chm` in the HHDC-Creator repo).
 
 ## Limitations and Risks
-- Forward model parameters (beam diameter, noise levels, output resolution, altitude) control task difficulty; document the values used per sample in `metadata`.
+- Forward model parameters (beam diameter, noise levels, output resolution, altitude) control task difficulty; we recommend documenting the values you use per experiment (e.g., in your own metadata/config). In the original HHDC-Creator pipeline these are stored per-sample in metadata, but this HF dataset does not include that field.
 - Outputs are simulated; real sensor artifacts (boresight errors, occlusions, calibration drift) are not modeled.
 - NEON LiDAR is collected over North America; models may not generalize to other biomes or sensor geometries without adaptation.