AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification

TL;DR

This paper introduces AID, a large-scale aerial scene classification dataset with over ten thousand images, aiming to facilitate the development of more advanced algorithms beyond the limitations of existing smaller datasets.

Contribution

The paper presents AID, a new extensive dataset for aerial scene classification, and provides baseline performance analysis of current methods on this benchmark.

Findings

Deep learning approaches outperform traditional methods on AID.

Existing datasets are too small, leading to saturated results.

AID enables more realistic evaluation of scene classification algorithms.

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 remote sensing area and numerous algorithms have been proposed for this task, including many machine learning and data-driven approaches. However, the existing datasets for aerial scene classification like UC-Merced dataset and WHU-RS19 are with 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 Dataset (AID): a large-scale dataset 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 ten thousands 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.