Aircraft detection from satellite imagery using synthetic data

This paper explores the advancement of object detection models within the domain of satellite imagery analysis, focusing on the innovative application of synthetically generated datasets to enhance model performance. Motivated by the inherent challenges of manual dataset annotation, such as errors, limited variability, and geographical biases, this study employs synthetic data generation techniques to create a diverse dataset by overlaying 3D models of 31 different aircraft types onto satellite imagery, creating a dataset of 5000 images containing 27,375 aircraft. This dataset includes a variety of environmental conditions and image transforms aimed at training a more robust and generalizable object detection model. Using a pre-trained model based on the YOLO v8x architecture, an extensive comparison of fine-tuned models trained separately on traditional manually annotated datasets was compared with a fine-tuned model trained on the synthetic dataset. The results show the model trained with synthetic data achieves a performance within 2% of the other models, detecting actual aircraft in diverse circumstances, despite only being trained on synthetic data derived from 3D models. By providing a scalable, efficient, and accurate method for training object detection models, this research demonstrates the possibility of detecting previously unseen objects based only on a 3D model.