AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification

TLDR

Aerial scene classification, which automatically labels high‑resolution remote sensing images, is a core task in remote sensing, yet existing datasets such as UC‑Merced and WHU‑RS19 are small and have saturated results, limiting algorithmic progress. This paper introduces AID, a large‑scale aerial image dataset designed to advance the state of the art in remote‑sensing scene classification. AID was created by collecting and annotating over 10,000 aerial scene images, and the authors also provide a comprehensive review of existing classification techniques and recent deep‑learning methods. The authors present baseline performance analyses of typical and deep‑learning approaches on AID, offering a reference for future research.

Abstract

Aerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. In recent years, it has become an active task in the remote sensing area, and numerous algorithms have been proposed for this task, including many machine learning and data-driven approaches. However, the existing data sets for aerial scene classification, such as UC-Merced data set and WHU-RS19, contain relatively small sizes, and the results on them are already saturated. This largely limits the development of scene classification algorithms. This paper describes the Aerial Image data set (AID): a large-scale data set for aerial scene classification. The goal of AID is to advance the state of the arts in scene classification of remote sensing images. For creating AID, we collect and annotate more than 10000 aerial scene images. In addition, a comprehensive review of the existing aerial scene classification techniques as well as recent widely used deep learning methods is given. Finally, we provide a performance analysis of typical aerial scene classification and deep learning approaches on AID, which can be served as the baseline results on this benchmark.