Aerial Scene Classification

Aerial scene classification aims to automatically label aerial images (from drones or satellites) with semantic information, such as identifying land cover types or objects within a scene. Current research emphasizes improving the accuracy and efficiency of classification models, often employing convolutional neural networks (CNNs) and incorporating techniques like multi-view learning, multi-instance learning, and efficient model architectures for resource-constrained platforms like UAVs. This field is crucial for applications ranging from environmental monitoring and disaster response to urban planning and precision agriculture, driving the development of novel datasets and algorithms for improved aerial image understanding.