README.md

---
license: cc-by-nc-4.0
tags:
- robotics
- drone-navigation
- vision-language-navigation
- open-vocabulary-detection
- embodied-ai
- habitat-sim
- benchmark
- preview-sample
task_categories:
- object-detection
- depth-estimation
- robotics
size_categories:
- n<1K
pretty_name: "Yonder (NeurIPS reviewer sample)"
language:
- en
---

# Yonder Sample — NeurIPS 2026 Reviewer Preview

This is a **~500 MB sample** of the [Yonder](https://huggingface.co/datasets/astralhf/yonder)
drone navigation dataset, intended for fast inspection by NeurIPS reviewers and others
who want to verify the data format and content before downloading the full ~3.3 TB release.

## What's included

- **One HSSD scene:** `hssd-102343992`
- **50 consecutive waypoint NPZs** (`wp0000` through `wp0049`)
- **All 12 yaw orientations** per waypoint
- **All sensor modalities** present in the full dataset:
  stereo RGB (left/right), forward depth, landing camera, up-IR, down-IR,
  360° LiDAR, position, orientation
- **Semantic segmentation** for every waypoint (50 matching `*_semantics.npz` files)
- ~50 × 10 MB sensor + 50 × ~25 KB semantics ≈ **500 MB** total

## What's NOT in this sample

- **Multiple scenes** — by design. This sample is a single-scene slice. The full
  release spans **167 HSSD scenes**, all with semantic annotations. Other scene
  sources (ReplicaCAD, Replica, HM3D) considered during early collection have been
  excluded from the public release for license-compatibility reasons; see the
  [main dataset card](https://huggingface.co/datasets/astralhf/yonder) for details.
- **COCO bounding-box annotations** — derived programmatically from the semantic
  channels and shipped per-scene under `annotations/` on the main repo.

## Quick start

```python
from huggingface_hub import snapshot_download
import numpy as np

local = snapshot_download(repo_id="astralhf/yonder-sample", repo_type="dataset")

# Sensor data
data = np.load(f"{local}/hssd-102343992_wp0000.npz")
print(sorted(data.keys())[:10])
# ['down_ir', 'lidar360', 'orientation', 'position', 'up_ir',
#  'yaw000_forward_depth', 'yaw000_landing_cam', 'yaw000_left_rgb',
#  'yaw000_right_rgb', 'yaw001_forward_depth']

print("left_rgb yaw000:", data["yaw000_left_rgb"].shape, data["yaw000_left_rgb"].dtype)
# left_rgb yaw000: (480, 640, 3) uint8
print("forward_depth yaw000:", data["yaw000_forward_depth"].shape, data["yaw000_forward_depth"].dtype)
# forward_depth yaw000: (480, 640) float16
print("lidar360:", data["lidar360"].shape, data["lidar360"].dtype)
# lidar360: (1024, 16) float32

# Semantic segmentation (one file per waypoint, 12 yaw keys)
sem = np.load(f"{local}/hssd-102343992_wp0000_semantics.npz")
print(sorted(sem.keys())[:4])
# ['yaw000_semantic', 'yaw030_semantic', 'yaw060_semantic', 'yaw090_semantic']
print("semantic yaw000:", sem["yaw000_semantic"].shape, sem["yaw000_semantic"].dtype)
# semantic yaw000: (480, 640) uint16  (per-pixel instance ID)
```

## Visualizing a frame

```python
import numpy as np
import matplotlib.pyplot as plt

data = np.load("hssd-102343992_wp0000.npz")
sem  = np.load("hssd-102343992_wp0000_semantics.npz")
fig, axes = plt.subplots(1, 4, figsize=(20, 5))
axes[0].imshow(data["yaw000_left_rgb"]);                 axes[0].set_title("Left RGB")
axes[1].imshow(data["yaw000_right_rgb"]);                axes[1].set_title("Right RGB")
axes[2].imshow(data["yaw000_forward_depth"], cmap="plasma")
axes[2].set_title("Forward depth (m)")
axes[3].imshow(sem["yaw000_semantic"], cmap="tab20")
axes[3].set_title("Semantic instance IDs")
for a in axes: a.axis("off")
plt.tight_layout(); plt.savefig("yonder_sample.png", dpi=150)
```

## Going to the full dataset

```python
# Single scene from the full repo (~25 GB sensor + semantics)
snapshot_download(
    repo_id="astralhf/yonder",
    repo_type="dataset",
    allow_patterns=[
        "indoor/drone-data/augmented/hssd-102343992/*.npz",
        "semantics/hssd-102343992/*.npz",
        "annotations/hssd-102343992/*.json",
    ],
)

# Just the manifests (a few MB) to plan a custom download
snapshot_download(
    repo_id="astralhf/yonder",
    repo_type="dataset",
    allow_patterns="indoor/drone-data/augmented/*/manifest.json",
)
```

## License

CC-BY-NC-4.0 (inheriting HSSD's NonCommercial restriction). See the
[main dataset card](https://huggingface.co/datasets/astralhf/yonder) for full
licensing and Responsible AI considerations.

## Citation

```bibtex
@inproceedings{anonymous2026yonder,
  title  = {Yonder: A 4.65M-Frame Drone Navigation Dataset and the Cross-Simulator Generalization Gap},
  author = {Anonymous Author(s)},
  booktitle = {NeurIPS Datasets and Benchmarks Track},
  year   = {2026},
  note   = {Anonymized for double-blind review.}
}
```