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aerial scene classification

Aerial scene classification is a remote sensing and computer vision task that involves automatically assigning a predefined semantic category label to an entire high-resolution aerial or satellite image based on its overall scene content. Rather than detecting isolated objects or labeling individual pixels, this process analyzes the structural arrangements, spatial patterns, and contextual features across an entire image to identify broad land-use or land-cover types, such as airports, residential zones, forests, or industrial parks. Driven primarily by machine learning and deep neural networks capable of learning complex visual representations, aerial scene classification plays an essential role in practical applications including urban planning, environmental monitoring, resource management, and disaster response.

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

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

Gui-Song Xia, Jingwen Hu, Fan Hu, Baoguang Shi, Xiang Bai, Yanfei Zhong, Liangpei Zhang, Xiaoqiang Lu

OrganizationsHuazhong University of Science and TechnologyState Key Laboratory of Information Engineering in Surveying, Mapping and Remote SensingWuhan University

Why you should read this

Introduces the Aerial Image Dataset (AID), a large-scale benchmark of over ten thousand annotated images that overcomes performance saturation in smaller datasets by establishing baseline evaluations for deep learning models in remote sensing scene classification.

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.

Added

2026-09-16