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

This paper presents K-Vehicles, a new dataset for vehicle detection in remote sensing 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 comprises 15,168 cropped images of 1,024×1,024 pixels, annotated manually across seven vehicle categories: truck, fork-lift, machinery, pickup, tractor, car, and bus. It incorporates relevant challenges such as occlusion, scene clutter, intrascene variation, and variable lighting conditions, making it suitable for training and evaluating detection models in realistic scenarios. To assess its utility, we conduct a benchmarking study using four recent You Only Look Once (YOLO) architectures, from YOLOv9 to YOLOv12. The models demonstrate strong performance, with mAP@50:95 reaching approximately 70%, confirming the learnability of the dataset, while also revealing persistent challenges in complex cases. Visual analyses support the numerical results and highlight the presence of fine-grained classification errors and detection failures under visual ambiguity. It is released with annotations in YOLO format and includes predefined train, validation, and test splits, and can be further extended for broader remote sensing applications. The dataset is available at: https://github.com/Leo-Thomas/K-Vehicles