README.md

---
license: cc-by-4.0
pretty_name: Hyperheight Data Cube Denoising and Super-Resolution
task_categories:
- image-to-image
annotations_creators:
- author-generated
language:
- en
source_datasets:
- NEON AOP discrete return LiDAR (DP1.30003.001)
tags:
- lidar
- remote-sensing
- neon
- canopy
- compressive-sensing
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: cube
    dtype:
      array3_d:
        shape:
        - 128
        - 48
        - 48
        dtype: float32
  - name: filename
    dtype: string
  splits:
  - name: train
    num_bytes: 75339604065
    num_examples: 62535
  - name: validation
    num_bytes: 16143770600
    num_examples: 13400
  - name: test
    num_bytes: 16144975359
    num_examples: 13401
  download_size: 4642822201
  dataset_size: 107628350024
---

## 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. 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.

## 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

### 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.

If you want to replicate our **exact** results, you can use the reference cube provided at `SampleCube/gt2.npz`.

## Usage
```python
from datasets import load_dataset
import torch

from hhdc.forward_model import LidarForwardImagingModel  # or your actual import path (check scripts folder in this repo)

# Load dataset
ds = load_dataset("anfera236/HHDC", split="train")
ds.set_format(type="torch", columns=["cube"])

# 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, # set to zero for no background photons
    eta=0.5, # set to zero for no Gaussian noise
    ref_altitude=500.0,
    ref_photon_count=20.0,
)

sample = ds[0]

# High-res “clean” HHDC: [bins, H, W]
clean = 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; 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.

## 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}
}

@article{ramirez2025super,
  title={Super-Resolved 3D Satellite Lidar Imaging of Earth Via Generative Diffusion Models},
  author={Ramirez-Jaime, Andres and Porras-Diaz, Nestor and Arce, Gonzalo R and Stephen, Mark},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2025},
  publisher={IEEE}
}

@inproceedings{ramirez2025denoising,
  title={Denoising and Super-Resolution of Satellite Lidars Using Diffusion Generative Models},
  author={Ramirez-Jaime, Andres and Porras-Diaz, Nestor and Arce, Gonzalo R and Stephen, Mark},
  booktitle={2025 IEEE Statistical Signal Processing Workshop (SSP)},
  pages={1--5},
  year={2025},
  organization={IEEE}
}
```

## Maintainers
- Andres Ramirez-Jaime — aramjai@udel.edu