Satellite imagery object detection

[neptyko]

Hi,
I plan to use the YOLOv11 model in my research, which will focus on detecting various objects in commercial satellite imagery.

One of the main questions I have is about the enormous size of these images—approximately 50,000 by 50,000 pixels. How should I prepare the dataset to ensure YOLOv11 can work with it effectively? Should I annotate objects directly on such large images, or would it be better to split them into smaller tiles, e.g., 10,000 by 10,000 pixels? Does YOLOv11 have any limitations regarding image dimensions, both during training and detection?

Additionally, I plan to work with panchromatic images (training and testing the model). Could this potentially reduce the model's performance?

I would appreciate your help. Best regards.

[UltralyticsAssistant]

👋 Hello @neptyko, thank you for reaching out and for your interest in using YOLOv11 for satellite imagery detection 🚀! This is an automated response, but rest assured, an Ultralytics engineer will assist you soon.

In the meantime, we encourage you to explore our Docs for guidance on handling large image datasets and the model's capabilities. If this is a 🐛 Bug Report, please include a minimum reproducible example to help expedite debugging.

For questions regarding custom training, it would be helpful if you could provide additional information like dataset samples or training logs. This detail can assist in giving you a more precise answer. Additionally, ensure you are following our Tips for Best Training Results.

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[glenn-jocher]

@neptyko to effectively use YOLO11 with large satellite images, it's recommended to divide the images into smaller, manageable tiles (e.g., 10,000x10,000 pixels) and annotate objects within these tiles, as YOLO11 may not handle extremely large dimensions like 50,000x50,000 pixels efficiently due to memory constraints and training limitations. For panchromatic images, ensure proper normalization and consider fine-tuning a pretrained model to address differences in color space, as limited image channels can potentially impact performance. You can find more guidance on dataset preparation and training in the YOLO11 training guide.