AID++: An Updated Version of AID on Scene Classification
This work addresses the need for more comprehensive datasets in remote sensing to better train deep learning models, though it is incremental as it builds upon an existing dataset.
The authors tackled the problem of limited scale and diversity in existing aerial image datasets for scene classification by introducing AID++, a larger-scale dataset with over 400,000 semi-automatically annotated image samples, which serves as a promising benchmark for evaluating CNN models.
Aerial image scene classification is a fundamental problem for understanding high-resolution remote sensing images and has become an active research task in the field of remote sensing due to its important role in a wide range of applications. However, the limitations of existing datasets for scene classification, such as the small scale and low-diversity, severely hamper the potential usage of the new generation deep convolutional neural networks (CNNs). Although huge efforts have been made in building large-scale datasets very recently, e.g., the Aerial Image Dataset (AID) which contains 10,000 image samples, they are still far from sufficient to fully train a high-capacity deep CNN model. To this end, we present a larger-scale dataset in this paper, named as AID++, for aerial scene classification based on the AID dataset. The proposed AID++ consists of more than 400,000 image samples that are semi-automatically annotated by using the existing the geographical data. We evaluate several prevalent CNN models on the proposed dataset, and the results show that our dataset can be used as a promising benchmark for scene classification.