{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/aid-a-benchmark-dataset-for-performance","title":"AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification","arxiv_id":"1608.05167","date":"2016-08-18","proceeding":null,"authors":["Gui-Song Xia","Jingwen Hu","Fan Hu","Baoguang Shi","Xiang Bai","Yanfei Zhong","Liangpei Zhang"],"abstract":"Aerial scene classification, which aims to automatically label an aerial\nimage with a specific semantic category, is a fundamental problem for\nunderstanding high-resolution remote sensing imagery. In recent years, it has\nbecome an active task in remote sensing area and numerous algorithms have been\nproposed for this task, including many machine learning and data-driven\napproaches. However, the existing datasets for aerial scene classification like\nUC-Merced dataset and WHU-RS19 are with relatively small sizes, and the results\non them are already saturated. This largely limits the development of scene\nclassification algorithms. This paper describes the Aerial Image Dataset (AID):\na large-scale dataset for aerial scene classification. The goal of AID is to\nadvance the state-of-the-arts in scene classification of remote sensing images.\nFor creating AID, we collect and annotate more than ten thousands aerial scene\nimages. In addition, a comprehensive review of the existing aerial scene\nclassification techniques as well as recent widely-used deep learning methods\nis given. Finally, we provide a performance analysis of typical aerial scene\nclassification and deep learning approaches on AID, which can be served as the\nbaseline results on this benchmark.","url_abs":"http://arxiv.org/abs/1608.05167v1","url_pdf":"http://arxiv.org/pdf/1608.05167v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"aid-a-benchmark-dataset-for-performance","repo_url":"https://github.com/MLEnthusiast/MHCLN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"aerial-scene-classification","task_name":"Aerial Scene Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"scene-classification","task_name":"Scene Classification"}],"methods":[],"datasets_introduced":[{"slug":"aid","name":"AID","full_name":"Aerial Image Dataset"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1608.05167","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}