Update README.md

README.md

@@ -33,3 +33,97 @@ dataset_info:
   download_size: 4642822201
   dataset_size: 107628350024
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+
+## Hyperheight Data Cube Denoising and Super-Resolution
+
+![Explanation](images/Explanation.png)
+
+## 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`.
+- 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.
+
+## Supported Tasks
+- Denoising of LiDAR photon-count hyperheight data cubes.
+- Super-resolution / resolution enhancement of lidar volumes.
+- 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.
+
+## Usage
+```python
+from datasets import load_dataset
+import torch
+
+from hhdc.forward_model import LidarForwardImagingModel  # or your actual import path
+
+# Load dataset
+ds = load_dataset("anfera236/HHDC", split="train")
+
+# Instantiate the LiDAR forward model (use your actual parameters)
+forward_model = LidarForwardImagingModel(
+    input_res_m=(2.0, 2.0),
+    output_res_m=(3.0, 6.0),
+    footprint_diameter_m=10.0,
+    b=0.1,
+    eta=0.5,
+    ref_altitude=500.0,
+    ref_photon_count=20.0,
+)
+
+sample = ds[0]
+
+# High-res “clean” HHDC: [bins, H, W]
+clean = torch.tensor(sample["cube"])
+
+# Low-res noisy measurement generated by the forward model: [bins, H_low, W_low]
+noisy = forward_model(clean)
+
+# Example: train a denoising/super-res model (my_model: noisy -> clean)
+pred = my_model(noisy.unsqueeze(0))       # [1, bins, H, W] ideally
+loss = loss_fn(pred, clean.unsqueeze(0))  # shapes must match
+loss.backward()
+```
+
+## Evaluation
+![AllPercentiles](images/AllPercentiles.png)
+
+- 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`.
+- 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.
+
+## Licensing
+- Derived from NEON AOP discrete-return LiDAR (DP1.30003.001). Follow the NEON Data Usage and Citation Policy and cite the original survey months/sites used.
+- Include the citation for the Hyperheight paper when publishing results that use this dataset.
+
+## Citation
+```
+@article{ramirez2024hyperheight,
+  title={Hyperheight lidar compressive sampling and machine learning reconstruction of forested landscapes},
+  author={Ramirez-Jaime, Andres and Pena-Pena, Karelia and Arce, Gonzalo R and Harding, David and Stephen, Mark and MacKinnon, James},
+  journal={IEEE Transactions on Geoscience and Remote Sensing},
+  volume={62},
+  pages={1--16},
+  year={2024},
+  publisher={IEEE}
+}
+```
+
+## Maintainers
+- Andres Ramirez-Jaime — aramjai@udel.edu
+