Your question is Sampling and Losses for Class Imbalance. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How do you handle class imbalance in datasets involving large-scale imagery?
Keep your answer focused on practical training choices, not theory alone. Explain how you would sample data, choose losses, and evaluate whether the minority class is being learned without overfitting.