Aerial Image Segmentation

Aerial image segmentation aims to automatically partition aerial images into meaningful regions, such as buildings, roads, or trees, facilitating efficient analysis of large-scale geographic data. Current research emphasizes improving segmentation accuracy by addressing challenges like class imbalance, limited annotated data, and variations in object scale and orientation, often employing transformer-based architectures, generative models for data augmentation, and novel loss functions to optimize model performance. This field is crucial for applications ranging from urban planning and environmental monitoring to archaeological site analysis and autonomous navigation, offering significant advancements in automated image interpretation and geographic information systems.