K-Vehicles: A Remote Sensing Dataset for Vehicle Detection in Aerial Imagery

Announcements

• K-Vehicles is now available for download at Hugging Face
• K-Vehicles has been accepted at the Second IEEE/CVF Workshop on Computer Vision for Geospatial Image Analysis (GeoCV) @ WACV 2026 📣📣📣
• K-Vehicles is under review (download option will be available after publication)

Dataset Details

We present K-Vehicles, a new dataset for vehicle detection in aerial imagery. It is built from high-resolution RGB images captured by a Cessna aircraft over diverse real-world environments, including highways, agricultural fields, and industrial zones. The dataset encompasses seven vehicle categories: truck, forklift, machinery, pickup, tractor, car, and bus. It incorporates relevant challenges such as occlusion, scene clutter, intra-scene variation, and variable lighting conditions, making it suitable for training and evaluating object detection models in realistic scenarios.

K-Vehicles consists of 15,168 cropped images, each with a fixed resolution of 1024x1024 pixels. These patches were obtained from aerial images captured under diverse conditions. The dataset is split into training, validation, and test subsets following an 80-10-10 proportion, resulting in 12,134 images for training, and 1,517 images for both validation and test sets. Annotations were generated in YOLO format, with each object instance described by its class and normalized bounding box coordinates. In total, the dataset includes 63,233 annotated instances, distributed as follows: 50,993 in the training set, 5,961 in the validation set, and 6,279 in the test set. The summary of K-Vehicles is as follows:

Paper

You can access the complete report of K-Vehicles here

Citation

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