Overhead Imagery

Overhead imagery analysis uses computer vision to extract information from aerial and satellite images, primarily aiming for automated object detection, segmentation, and mapping. Current research focuses on improving model efficiency and accuracy using deep learning architectures like convolutional neural networks (CNNs) and transformers, often incorporating graph neural networks for spatial relationship modeling and exploring the effectiveness of different labeling strategies to reduce data annotation costs. This field is significant for its applications in diverse areas such as urban planning, infrastructure monitoring, environmental assessment, and even poverty estimation, offering efficient and scalable solutions for data acquisition and analysis.