Add full dataset card

README.md

@@ -1,89 +1,45 @@
 ---
-annotations_creators: []
-language: en
 license: cc-by-nc-sa-4.0
-size_categories:
-- n<1K
-task_categories: []
-task_ids: []
-pretty_name: HighwayScene
+task_categories:
+  - other
 tags:
-- background-subtraction
-- fiftyone
-- intelligent-transportation-systems
-- lidar
-- mcap
-- multimodal
-- multimodal
-- point-cloud
-- roadside-perception
-description: HighwayScene is a synchronized multi-LiDAR roadside dataset recorded
-  at a highway construction site. Three statically mounted sensors — an Ouster OS0
-  (rotating ToF, 10 Hz), an Aeva Aeries II (FMCW, 10 Hz), and a Blickfeld QB2 (solid-state
-  ToF, 5 Hz) — observe the same free-flowing highway traffic scene simultaneously.
-  This FiftyOne version re-encodes the original protobuf records as MCAP episodes
-  for exploration in the FiftyOne multimodal viewer.
-dataset_summary: '
-
-
-
-
-  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 30 samples.
+  - fiftyone
+  - multimodal
+  - lidar
+  - roadside-perception
+  - background-subtraction
+  - intelligent-transportation-systems
+  - point-cloud
+  - mcap
+pretty_name: HighwayScene
+size_categories:
+  - n<1K
+dataset_summary: |
+  HighwayScene is a synchronized multi-LiDAR roadside dataset recorded at a highway construction site. Three statically mounted sensors — an Ouster OS0 (rotating ToF, 10 Hz), an Aeva Aeries II (FMCW, 10 Hz), and a Blickfeld QB2 (solid-state ToF, 5 Hz) — observe the same free-flowing highway traffic scene simultaneously. This FiftyOne version re-encodes the original protobuf records as MCAP episodes for exploration in the FiftyOne multimodal viewer.
 
+  The dataset contains 30 episodes (~20 seconds each), split into train (20), val (5), and test (5). Each episode carries three synchronized point-cloud streams with per-point fields including radial velocity (Aeva) and beam identifiers (Ouster, Blickfeld).
 
   ## Installation
 
-
-  If you haven''t already, install FiftyOne:
-
-
   ```bash
-
   pip install -U fiftyone
-
   ```
 
-
   ## Usage
 
-
   ```python
-
   import fiftyone as fo
-
   from fiftyone.utils.huggingface import load_from_hub
 
-
-  # Load the dataset
-
-  # Note: other available arguments include ''max_samples'', etc
-
   dataset = load_from_hub("harpreetsahota/HighwayScene")
-
-
-  # Launch the App
-
   session = fo.launch_app(dataset)
-
   ```
-
-  '
 ---
 
 # Dataset Card for HighwayScene
 
-<!-- Provide a quick summary of the dataset. -->
-
-
-
-
-
-This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 30 samples.
-
 ## Installation
 
-If you haven't already, install FiftyOne:
-
 ```bash
 pip install -U fiftyone
 ```
@@ -94,143 +50,175 @@ pip install -U fiftyone
 import fiftyone as fo
 from fiftyone.utils.huggingface import load_from_hub
 
-# Load the dataset
-# Note: other available arguments include 'max_samples', etc
 dataset = load_from_hub("harpreetsahota/HighwayScene")
-
-# Launch the App
 session = fo.launch_app(dataset)
 ```
 
-
 ## Dataset Details
 
 ### Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
+HighwayScene is a synchronized multi-LiDAR roadside dataset introduced for the cross-sensor background subtraction benchmark in "Beam-Wise Statistical Background Subtraction for Static Roadside LiDAR: A Cross-Sensor Benchmark Study" (ITSC 2026). Three statically mounted sensors with different sensing principles observe the same highway construction-site scene simultaneously:
 
+| Sensor | Principle | Rate |
+|--------|-----------|------|
+| Ouster OS0 | Rotating time-of-flight | 10 Hz |
+| Aeva Aeries II | FMCW | 10 Hz |
+| Blickfeld QB2 | Solid-state time-of-flight | 5 Hz |
 
+The scene features consistently high traffic volume under a legally enforced 40 km/h speed limit. The dataset supports research on background modeling, dynamic-point segmentation, and cross-sensor benchmarking for static roadside LiDAR.
 
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** cc-by-nc-sa-4.0
+This FiftyOne version converts the original protobuf `.pb` records to MCAP format so each episode can be explored interactively in the FiftyOne multimodal viewer, with all three sensor streams on a shared 20-second timeline.
 
-### Dataset Sources [optional]
+- **Curated by:** Alexander Baumann, Marcel Voßhans, Thao Dang — Institute for Intelligent Systems, Esslingen University of Applied Sciences, Germany
+- **Funded by:** [More Information Needed]
+- **Shared by:** Institute for Intelligent Systems (IIS), Esslingen University of Applied Sciences
+- **Language(s):** N/A
+- **License:** CC BY-NC-SA 4.0 (non-commercial use only)
 
-<!-- Provide the basic links for the dataset. -->
+### Dataset Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://huggingface.co/datasets/iis-esslingen/HighwayScene (original protobuf records)
+- **Paper:** https://arxiv.org/abs/2608.14868
+- **Demo:** https://highwayscene.github.io/
 
 ## Uses
 
-<!-- Address questions around how the dataset is intended to be used. -->
-
 ### Direct Use
 
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
+- Benchmarking beam-wise statistical background subtraction methods across heterogeneous LiDAR technologies
+- Cross-sensor evaluation of static/dynamic point-cloud segmentation
+- Research on roadside LiDAR perception: traffic monitoring, infrastructure-based sensing, inter-vehicle distance estimation
+- Multi-modal LiDAR data exploration via the FiftyOne App's 3D tile and shared timeline
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
+- Moving-sensor or ego-vehicle applications — the benchmark assumes a statically mounted sensor with a fixed scan pattern
+- Commercial use — the CC BY-NC-SA 4.0 license prohibits commercial applications
+- Scenes with substantial long-term structural changes, changed sensor poses, or dynamically varying scan patterns
 
 ## Dataset Structure
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-
-[More Information Needed]
+### FiftyOne Dataset Topology
+
+**Media type:** `multimodal` (MCAP files)
+**Sample count:** 30 episodes
+**Splits:** train (20), val (5), test (5), encoded as the `split` string field and queryable via `dataset.match(fo.ViewField("split") == "test")`
+
+Each FiftyOne sample points to one `.mcap` file covering approximately 20 seconds of synchronized multi-sensor recording. The MCAP file contains three point-cloud topics (`/ouster/points`, `/aeva/points`, `/blickfeld/points`) and three static identity frame transforms on `/tf_static`. All topics use the `foxglove.PointCloud` schema and are rendered in the FiftyOne App's **3D tile** on a shared timeline.
+
+### Sample Fields
+
+| Field | FiftyOne type | Description |
+|-------|---------------|-------------|
+| `filepath` | `StringField` | Absolute path to the `.mcap` episode file |
+| `split` | `StringField` | Benchmark split: `"train"`, `"val"`, or `"test"` |
+| `record_name` | `StringField` | Original `.pb` filename stem (encodes recording date and global frame-ID range) |
+| `first_frame_id` | `IntField` | First global frame ID in this episode (1-based, dataset-wide) |
+| `last_frame_id` | `IntField` | Last global frame ID in this episode |
+| `duration_s` | `FloatField` | Episode duration in seconds (~19.9 s for 200-frame episodes) |
+| `message_count` | `IntField` | Total MCAP message count across all topics in this episode |
+| `ouster_frames` | `IntField` | Number of Ouster OS0 point-cloud messages (10 Hz; 200 per full episode) |
+| `aeva_frames` | `IntField` | Number of Aeva Aeries II point-cloud messages (10 Hz; 194–200 per episode) |
+| `blickfeld_frames` | `IntField` | Number of Blickfeld QB2 point-cloud messages (5 Hz; 95–101 per episode) |
+| `topics` | `ListField(StringField)` | MCAP topic names present in the episode |
+| `schemas` | `ListField(StringField)` | MCAP schema names used (`foxglove.PointCloud`, `foxglove.FrameTransform`) |
+| `has_pointcloud` | `BooleanField` | `True` for all 30 episodes |
+| `has_image` | `BooleanField` | `False` — no camera data in this dataset |
+| `has_gps` | `BooleanField` | `False` — no GPS data |
+| `has_imu` | `BooleanField` | `False` — no IMU data |
+| `has_logs` | `BooleanField` | `False` — no log messages |
+
+### MCAP Topic Structure (per episode)
+
+| Topic | Schema | Rate | Per-point fields |
+|-------|--------|------|-----------------|
+| `/ouster/points` | `foxglove.PointCloud` | 10 Hz | `x`, `y`, `z` (float32); `intensity`, `ambient`, `reflectivity` (float32); `channel_id` (float32) |
+| `/aeva/points` | `foxglove.PointCloud` | 10 Hz | `x`, `y`, `z` (float32); `intensity`, `ambient`, `reflectivity`, `velocity` (float32, radial m/s) |
+| `/blickfeld/points` | `foxglove.PointCloud` | 5 Hz | `x`, `y`, `z` (float32); `reflectivity`, `channel_id`, `horizontal_id` (float32) |
+| `/tf_static` | `foxglove.FrameTransform` | static | Identity transforms for `ouster`, `aeva`, `blickfeld` frames relative to `world` |
+
+Typical point counts per frame: Ouster ~45K, Aeva ~92K, Blickfeld ~12K. Blickfeld has no `intensity` or `ambient` channel. Aeva's `velocity` field (radial velocity in m/s) is the unique FMCW capability enabling velocity-based dynamic-point labeling.
+
+### Ground Truth
+
+No per-frame label files are stored in the FiftyOne dataset. The benchmark computes static/dynamic labels at evaluation time from parameters in `metadata/ground_truth.yaml` (in the original repository):
+- **Ouster and Blickfeld**: lane-aligned 3D bounding-box ROIs define dynamic regions; any point inside is labeled dynamic.
+- **Aeva**: a point is labeled dynamic if its measured radial velocity exceeds 1 m/s.
+
+### Parsing Decisions
+
+- Original records are protobuf `.pb` files decoded with `highwayscene-proto==1.0.0`. Each `.pb` → one `.mcap` using `foxglove-sdk==0.26.0`.
+- All uint16 auxiliary channels (intensity, ambient, reflectivity, channel_id, horizontal_id) are cast to float32 in the MCAP point-cloud buffers.
+- Points with range ≤ 0.5 m are filtered (zero-return guard).
+- Static transforms use `foxglove.FrameTransform` with no embedded timestamp, placing them in the viewer's static store for the full episode duration.
+- No extrinsic sensor-to-sensor calibration is provided by the dataset authors; all `world → sensor` transforms are identity.
+- Blickfeld operates at 5 Hz: frames where the sensor is absent are simply omitted from the `/blickfeld/points` topic.
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
+Existing roadside LiDAR datasets focus on object detection or semantic segmentation with class-based labels, and are typically limited to a single sensor or scene. HighwayScene was created to fill the gap in systematic cross-sensor evaluation for static background subtraction — providing simultaneous recordings from three heterogeneous LiDAR technologies under real highway traffic conditions, with reproducible annotation strategies that do not require manual point-wise labeling.
 
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
 #### Data Collection and Processing
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
+Data was recorded at a German highway construction site with a legally enforced 40 km/h speed limit. Sensors were mounted statically on roadside infrastructure. Recording rate is 10 Hz for the Ouster and Aeva sensors and 5 Hz for the Blickfeld. All streams are temporally synchronized. 5,998 global frames are captured across the full recording session (Feb 9, 2026), partitioned into train (frames 1–4000), val (4001–5000), and test (5001–5998) splits using a strict non-overlapping protocol.
 
 #### Who are the source data producers?
 
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-
-[More Information Needed]
-
-### Annotations [optional]
+Institute for Intelligent Systems (IIS), Esslingen University of Applied Sciences, Germany. Contact: `alexander.baumann@hs-esslingen.de`.
 
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+### Annotations
 
 #### Annotation process
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+Static/dynamic ground-truth labels are not stored per frame in the dataset records. Instead, two reproducible annotation strategies are defined in `metadata/ground_truth.yaml`:
 
-[More Information Needed]
+1. **Geometry-based (Ouster, Blickfeld):** For each traffic lane, a static 3D bounding-box ROI aligned with the roadway is manually defined. Under empty-scene conditions no LiDAR returns fall inside these volumes. Since vehicles are constrained by concrete barriers, any return inside a lane volume originates from dynamic traffic. Points inside are labeled dynamic; all others static.
 
-#### Who are the annotators?
+2. **Velocity-based (Aeva):** A point is labeled dynamic if its measured FMCW radial velocity exceeds 1 m/s. This strategy requires no scene geometry and illustrates the FMCW sensor's self-annotating capability.
 
-<!-- This section describes the people or systems who created the annotations. -->
+#### Who are the annotators?
 
-[More Information Needed]
+Annotation parameters (lane ROIs, ground planes) were defined by the dataset authors. No manual point-wise labeling was performed — labels are reproducibly derived from geometry and physics.
 
 #### Personal and Sensitive Information
 
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
+LiDAR point clouds do not record visually identifiable information (faces, license plates). No personal data is present in this dataset.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@inproceedings{baumann2026beamwise,
+  author    = {Alexander Baumann and Marcel Vo{\ss}hans and Thao Dang},
+  title     = {Beam-Wise Statistical Background Subtraction for Static
+               Roadside {LiDAR}: A Cross-Sensor Benchmark Study},
+  booktitle = {IEEE International Conference on Intelligent Transportation
+               Systems (ITSC)},
+  year      = {2026}
+}
+```
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
+Baumann, A., Voßhans, M., & Dang, T. (2026). Beam-Wise Statistical Background Subtraction for Static Roadside LiDAR: A Cross-Sensor Benchmark Study. *IEEE International Conference on Intelligent Transportation Systems (ITSC)*.
 
-## More Information [optional]
+## More Information
 
-[More Information Needed]
+- Original dataset (protobuf format): https://huggingface.co/datasets/iis-esslingen/HighwayScene
+- Background subtraction code: https://github.com/HighwayScene/roadside-lidar-background-subtraction
+- Protobuf reader: https://github.com/HighwayScene/highwayscene-proto
+- Project page: https://highwayscene.github.io/
 
-## Dataset Card Authors [optional]
+## Dataset Card Authors
 
-[More Information Needed]
+Harpreet Sahota
 
 ## Dataset Card Contact
 
-[More Information Needed]
+harpreetsahota07@gmail.com