| | --- |
| | license: cc-by-nc-4.0 |
| | language: |
| | - en |
| | pipeline_tag: object-detection |
| | library_name: mmdetection |
| | --- |
| | ## Introduction |
| | We introduce a real-world aerial view dataset, LINZ, captured in Selwyn (New Zealand). The dataset has ground sampling distance (GSD) of 12.5 cm per px and has been sampled to 112 px × 112 px image size. For data annotation, we label only the small vehicle centers. To leverage the abundance of bounding box-based open-source object detection frameworks, we define a fixed-size ground truth bounding box of 42.36 px × 42.36 px centered at each vehicle. Annotations are provided in COCO format [x, y, w, h], where "small" in the annotation json files denotes the small vehicle class and (x, y) denotes the top-left corner of the bounding box. We use AP50 as the evaluation metric. |
| |
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| | ## Model Usage |
| | This folder contains four detectors trained on Real LINZ data and tested on Real LINZ data, along with configuration files we use for training and testing. |
| |
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| | ## References |
| |
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| | ➡️ **Paper:** [Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision](https://arxiv.org/abs/2507.20976) |
| | ➡️ **Project Page:** [Webpage](https://humansensinglab.github.io/AGenDA/) |
| | ➡️ **Data:** [AGenDA](https://github.com/humansensinglab/AGenDA/tree/main/Data) |
