anfera236/HHDC

Hyperheight Data Cube Denoising and Super-Resolution

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

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(
    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

• 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