CADOT: Cityscape Aerial Image Dataset For Object Detection

This paper presents our Cityscape Aerial image Dataset for Object deTection (CADOT), a new benchmark dataset for object detection, based on raw aerial images sourced from the IGN website and focused on a specific area within the Paris department. CADOT consists of cropped sub-images with irrelevant areas carefully excluded. Due to image complexity, an algorithm with human validation was developed for accurate annotation. CADOT contains 106691 object annotations across 14 categories in COCO format, spanning transportation infrastructure to urban features. We provide distinct splits for training, validation, and testing. The dataset’s complexity is highlighted through comparative performance evaluations against well-established detection models using other existing aerial image datasets such as DIOR. Our results show that CADOT offers a significantly greater challenge than other benchmark aerial image datasets. The CADOT dataset is accessible via CADOT .