Aerial Image Dataset

Aerial image datasets are crucial for training and evaluating computer vision models applied to diverse real-world problems, ranging from environmental monitoring to autonomous navigation. Current research focuses on creating large, accurately annotated datasets for specific tasks like building extraction, lane detection, and agricultural pattern analysis, often employing deep learning models such as convolutional neural networks (CNNs) and transformers. These datasets are driving advancements in semantic segmentation, object detection, and uncertainty calibration techniques, ultimately improving the accuracy and reliability of automated systems across various domains. The availability of high-quality, task-specific datasets is essential for progress in remote sensing, precision agriculture, and other fields reliant on aerial imagery analysis.