The past years have witnessed great progress on remote sensing (RS) image interpretation and its wide applications. With RS images becoming more accessible than ever before, there is an increasing demand for the automatic interpretation of these images. In this context, the benchmark datasets serve as essential prerequisites for developing and testing intelligent interpretation algorithms. After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation. Specifically, we first analyze the current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations. We then present the general guidance on creating benchmark datasets in efficient manners. Following the presented guidance, we also provide an example on building RS image dataset, i.e., Million-AID, a new large-scale benchmark dataset containing a million instances for RS image scene classification. Several challenges and perspectives in RS image annotation are finally discussed to facilitate the research in benchmark dataset construction. We do hope this paper will provide the RS community an overall perspective on constructing large-scale and practical image datasets for further research, especially data-driven ones.
Annotated Datasets for RS Image Interpretation The interpretation of RS images has been playing an increasingly important role in a large diversity of applications, and thus, has attracted remarkable research attentions. Consequently, various datasets have been built to advance the development of interpretation algorithms for RS images. Covering literature published over the past decade, we perform a systematic review of the existing RS image datasets concerning the current mainstream of RS image interpretation tasks, including scene classification, object detection, semantic segmentation and change detection.
Artificial Intelligence, Computer Vision, Image Processing, Deep Learning, Satellite Image, Remote Sensing
source of images available in the link: https://www.kaggle.com/datasets/mahmoudreda55/satellite-image-classification