---
license: cc-by-4.0
base_model:
- Ultralytics/YOLOv8
base_model_relation: finetune
pipeline_tag: object-detection
library_name: ultralytics
num_parameters: 11137922
github: https://github.com/rfonod/geo-trax
language:
- en
tags:
- ultralytics
- yolov8
- object-detection
- aerial-imagery
- drone
- vehicle-detection
- birds-eye-view
- geo-trax
- trajectory
- urban-traffic
- tracking
- arxiv:2411.02136
datasets:
- rfonod/songdo-vision
- Voxel51/VisDrone2019-DET
- detection-datasets/coco
model-index:
- name: rfonod/geo-trax/geotrax_hbb_yolov8s_1920_v1
  results:
  - task:
      type: object-detection
    dataset:
      type: rfonod/songdo-vision
      name: Songdo Vision
      split: test
    metrics:
      - type: precision  # mAP@0.5 not available as a standard metric type on HF
        value: 0.951
        name: mAP@0.5
      - type: precision
        value: 0.711
        name: mAP@0.5:0.95
      - type: precision
        value: 0.911
        name: Precision
      - type: recall
        value: 0.935
        name: Recall
---

# Geo-trax: YOLOv8s Vehicle Detector for Drone BEV Imagery

[![GitHub](https://img.shields.io/badge/GitHub-geo--trax-blue?logo=github)](https://github.com/rfonod/geo-trax)
[![PyPI](https://img.shields.io/pypi/v/geo-trax)](https://pypi.org/project/geo-trax/)
[![Demo Space](https://img.shields.io/badge/🤗%20Space-Live%20Demo-yellow)](https://huggingface.co/spaces/rfonod/geo-trax)
[![License](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey)](LICENSE)
[![Paper](https://img.shields.io/badge/Journal-10.1016%2Fj.trc.2025.105205-blue)](https://doi.org/10.1016/j.trc.2025.105205)
[![arXiv](https://img.shields.io/badge/arXiv-2411.02136-b31b1b)](https://arxiv.org/abs/2411.02136)
[![Songdo Vision](https://img.shields.io/badge/🤗%20Dataset-rfonod%2Fsongdo--vision-yellow)](https://huggingface.co/datasets/rfonod/songdo-vision)
[![Songdo Traffic](https://img.shields.io/badge/🤗%20Dataset-rfonod%2Fsongdo--traffic-yellow)](https://huggingface.co/datasets/rfonod/songdo-traffic)
[![Website](https://img.shields.io/badge/REAL%20Lab-Geo--trax-informational)](https://www.real-lab.ch/geo-trax)
[![YouTube](https://img.shields.io/badge/YouTube-Demo-red?logo=youtube&logoColor=red)](https://youtu.be/gOGivL9FFLk)

This is the default detection model for **[Geo-trax](https://github.com/rfonod/geo-trax)**, a
comprehensive pipeline for extracting georeferenced vehicle trajectories from high-altitude drone
(bird's-eye view) video footage. The model detects vehicles in aerial imagery and underpins the
results reported in the associated [publication](https://doi.org/10.1016/j.trc.2025.105205).

![Geo-trax Output Visualization](https://raw.githubusercontent.com/rfonod/geo-trax/main/assets/geo-trax_visualization.webp)

🎬 This accelerated animation previews some of the capabilities of Geo-trax. Watch the full
demonstration (~4 min) on [YouTube](https://youtu.be/gOGivL9FFLk).

## Model Details

| Property | Value |
|---|---|
| Architecture | YOLOv8s (HBB, horizontal bounding boxes) |
| Input resolution | 1920 × 1920 px |
| Classes | 6 trained (4 primary + 2 auxiliary; see below) |
| Parameters | 11.1 M (11,137,922) |
| Framework | [Ultralytics](https://github.com/ultralytics/ultralytics) ≥ 8.4.64 |
| Trained on | 19,339 annotated aerial images (679,306 labeled instances); multi-stage, see [publication](https://doi.org/10.1016/j.trc.2025.105205) |
| Validated on | [Songdo Vision](https://doi.org/10.5281/zenodo.13828407) test set (1,084 images, 55,124 vehicle instances) |

### Classes and Detection Performance

Metrics reported on the [Songdo Vision](https://doi.org/10.5281/zenodo.13828407) test split
(1,084 images, 55,124 labeled vehicle instances). The `Instances` column is the per-class
support in the test set. See Table 3 of the
[publication](https://doi.org/10.1016/j.trc.2025.105205) for full results.

| ID | Label | Notes | Instances | Precision | Recall | mAP@50 | mAP@50-95 |
|---|---|---|---:|---|---|---|---|
| 0 | Car | incl. vans | 49,508 | 0.979 | 0.981 | 0.992 | 0.835 |
| 1 | Bus | | 1,759 | 0.952 | 0.977 | 0.988 | 0.826 |
| 2 | Truck | | 3,052 | 0.887 | 0.916 | 0.935 | 0.722 |
| 3 | Motorcycle | | 805 | 0.827 | 0.866 | 0.888 | 0.463 |
| 4 | Pedestrian | not evaluated | n/a | n/a | n/a | n/a | n/a |
| 5 | Bicycle | not evaluated | n/a | n/a | n/a | n/a | n/a |
| **All** | | | **55,124** | **0.911** | **0.935** | **0.951** | **0.711** |

The model reaches **0.951 mAP@50** and **0.711 mAP@50-95** overall, with near-saturated accuracy
on cars and buses (mAP@50 ≥ 0.988). Trucks and especially motorcycles are harder: motorcycles are
small, sparse in the test set (805 instances), and the main driver of the lower mAP@50-95.

### Evaluation Plots

Precision-recall curves and the normalized confusion matrix on the Songdo Vision test set:

<table>
  <tr>
    <td align="center"><img src="assets/PR_curve.png" alt="Precision-Recall Curve" width="420"><br><sub>Precision-Recall Curve</sub></td>
    <td align="center"><img src="assets/confusion_matrix_normalized.png" alt="Normalized Confusion Matrix" width="420"><br><sub>Normalized Confusion Matrix</sub></td>
  </tr>
</table>

> **Note on pedestrian and bicycle classes:** The model was trained on pedestrian and bicycle
> instances; however, these classes are **not evaluated and not recommended for use**. They were
> underrepresented in the training data, are not annotated in the Songdo Vision dataset (making
> reliable evaluation impossible), and achieve poor detection performance in practice.

## How to Use

> 🚀 **Try it first, no install:** the [interactive 🤗 Space](https://huggingface.co/spaces/rfonod/geo-trax) runs this detector in your browser on your own aerial image or short clip.

### With Geo-trax (recommended)

This model is the default in Geo-trax and downloads automatically on first use:

```bash
pip install geo-trax
geotrax extract video.mp4         # detect, track, and stabilize; auto-downloads the model
geotrax batch video.mp4 --no-geo  # detect, track, and stabilize; skip georeferencing
geotrax batch video.mp4           # full pipeline including georeferencing (requires orthophotos)
geotrax batch video.mp4 --sahi    # optional SAHI inference for better small-object detection
```

See the Geo-trax [GitHub README](https://github.com/rfonod/geo-trax) for the full pipeline,
configuration options, and georeferencing.

### Direct Ultralytics inference (PyTorch)

```python
from ultralytics import YOLO
from huggingface_hub import hf_hub_download

weights = hf_hub_download(repo_id="rfonod/geo-trax", filename="geotrax_hbb_yolov8s_1920_v1.pt")
model = YOLO(weights)

results = model("drone_frame.jpg", imgsz=1920, conf=0.25, iou=0.45, classes=[0, 1, 2, 3])
results[0].show()
```

> **Tip:** The model was trained and validated at **1920 px** input resolution. Downscaling to
> 1280 px is possible with a small accuracy trade-off; going below 960 px significantly degrades
> detection of small vehicles (motorcycles, distant cars). Pass `classes=[0, 1, 2, 3]` to
> restrict inference to the four evaluated classes and suppress unreliable predictions.

### ONNX inference

 An ONNX export (opset 12, static 1920 × 1920 input) is available for deployment without a PyTorch dependency:

 ```python
import numpy as np
import onnxruntime as ort
from huggingface_hub import hf_hub_download

 onnx_path = hf_hub_download(repo_id="rfonod/geo-trax", filename="geotrax_hbb_yolov8s_1920_v1.onnx")
session = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])

 # Prepare input: BGR image resized/padded to 1920×1920, normalized to [0, 1]
img = np.random.rand(1, 3, 1920, 1920).astype(np.float32)  # replace with real image
outputs = session.run(None, {"images": img})
# outputs[0] shape: (1, 10, 75600) — 10 = 4 bbox coords + 6 class scores
```

## Training Data

Training followed a multi-stage strategy starting from **YOLOv8s weights pretrained on COCO**
as the initial foundation. Two successive stages were applied:

**Stage 1 (BASE):** The model was trained on a large, diverse collection drawn from eight
public aerial and drone datasets (CARPK, PUCPR+, CyCAR, UAVDT, HARPY, RAI4VD, UIT-ADrone,
and VisDrone) combined with the [Songdo Vision](https://doi.org/10.5281/zenodo.13828407)
dataset, totalling 19,339 training images with 679,306 annotations across 6 vehicle classes
(car, bus, truck, motorcycle, pedestrian, bicycle).

**Stage 2 (FINE):** The BASE-trained model was subsequently fine-tuned on a curated,
high-quality subset of 9,004 images with 321,368 annotations, emphasising accurate annotations
and higher-resolution images, again combined with
[Songdo Vision](https://doi.org/10.5281/zenodo.13828407), to yield the final weights released here.

**Training set composition** (annotations per class):

| Stage | Images | Annotations | Car | Bus | Truck | Motorcycle | Pedestrian | Bicycle |
|---|---:|---:|---:|---:|---:|---:|---:|---:|
| BASE | 19,339 | 679,306 | 561,666 | 15,587 | 28,830 | 44,512 | 24,239 | 4,472 |
| FINE |  9,004 | 321,368 | 266,745 |  8,047 | 14,305 | 30,925 |  1,260 |    86 |

[Songdo Vision](https://doi.org/10.5281/zenodo.13828407) comprises 5,419 annotated drone frames
(4,335 training / 1,084 test; 80/20 split) collected during a large-scale urban traffic
monitoring experiment in Songdo, South Korea. It covers four primary vehicle classes captured
at 140-150 m altitude by DJI Mavic 3 drones, contributing 217,311 training and 55,124 test
instances to the totals above.

**Training configuration:**

| Setting | Value |
|---|---|
| Initialization | YOLOv8s pretrained on COCO |
| Optimizer | SGD |
| Learning rate (initial / final factor) | 0.01 / 0.01 |
| Momentum | 0.937 |
| Weight decay | 0.0005 |
| Batch size | 8 |
| Early stopping | 50-epoch patience |
| Input resolution | 1920 × 1920 px (letterbox padding) |
| Mixed precision | AMP enabled |
| Augmentation | random scaling, translation, horizontal flip, mosaic, colour jitter, Gaussian/median blur, grayscale, CLAHE |

See the [publication](https://doi.org/10.1016/j.trc.2025.105205) for complete dataset statistics,
training details, and ablation results.

## Intended Use and Limitations

- **GSD assumption:** The bundled Geo-trax config assumes a ground sampling distance (GSD) of
  ~0.027 m/px (DJI Mavic 3, 4K, 140-150 m altitude). Adjust this value in the config for
  different hardware or flight altitudes.
- **Supported classes:** Car, bus, truck, and motorcycle (class IDs 0-3). The model was also
  trained on pedestrian and bicycle instances; however, these classes achieve poor detection
  performance and are not recommended for use (see the class table above). Geo-trax filters to
  the four primary classes by default; when using Ultralytics directly, pass
  `classes=[0, 1, 2, 3]` to suppress unreliable predictions.

## Related datasets and resources

- **Live demo**: interactive 🤗 Space — [`rfonod/geo-trax` (Spaces)](https://huggingface.co/spaces/rfonod/geo-trax)
- **Songdo Traffic**: the georeferenced vehicle-trajectory dataset this model helps produce via
  the Geo-trax pipeline:
  [`10.5281/zenodo.13828384`](https://doi.org/10.5281/zenodo.13828384) ·
  HF [`rfonod/songdo-traffic`](https://huggingface.co/datasets/rfonod/songdo-traffic)
- **Songdo Vision**: the vehicle-detection (annotated image) dataset used to train and validate
  this model: [`10.5281/zenodo.13828407`](https://doi.org/10.5281/zenodo.13828407) ·
  HF [`rfonod/songdo-vision`](https://huggingface.co/datasets/rfonod/songdo-vision)
- **Source video recordings** (not open access):
  [`10.5075/EPFL.20.500.14299/253923`](https://doi.org/10.5075/EPFL.20.500.14299/253923)
- **Publication**: *Transportation Research Part C* (2025):
  [`10.1016/j.trc.2025.105205`](https://doi.org/10.1016/j.trc.2025.105205) ·
  [arXiv:2411.02136](https://arxiv.org/abs/2411.02136)
- **Software**: Geo-trax: [github.com/rfonod/geo-trax](https://github.com/rfonod/geo-trax) ·
  Zenodo [`10.5281/zenodo.12119542`](https://doi.org/10.5281/zenodo.12119542) ·
  [demo video](https://youtu.be/gOGivL9FFLk)

## Citation

If you use this model, please cite the associated publication:

```bibtex
@article{fonod2025advanced,
  title   = {Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery},
  author  = {Fonod, Robert and Cho, Haechan and Yeo, Hwasoo and Geroliminis, Nikolas},
  journal = {Transportation Research Part C: Emerging Technologies},
  volume  = {178},
  pages   = {105205},
  year    = {2025},
  doi     = {10.1016/j.trc.2025.105205}
}
```

If you additionally use the [Geo-trax software](https://github.com/rfonod/geo-trax), please
also cite the specific version you used via its Zenodo record. For example, for version 1.3.0:

```bibtex
@software{fonod2026geo-trax,
  author  = {Fonod, Robert},
  title   = {Geo-trax: A Comprehensive Framework for Georeferenced Vehicle Trajectory Extraction from Drone Imagery},
  url     = {https://github.com/rfonod/geo-trax},
  doi     = {10.5281/zenodo.12119542},
  version = {1.3.0},
  year    = {2026}
}
```

## License

This model is released under the
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
license; see the [LICENSE](LICENSE) file for the full terms. The Geo-trax codebase is distributed
separately under the [MIT License](https://github.com/rfonod/geo-trax/blob/main/LICENSE).