mlsanityData to 7 Oct 2026

Deep Learning for Scene Classification: A Survey

arXiv 2101.10531 ↗First posted 26 Jan 2021cs.CVcited by 9

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Abstract (arXiv)

Scene classification, aiming at classifying a scene image to one of the predefined scene categories by comprehending the entire image, is a longstanding, fundamental and challenging problem in computer vision. The rise of large-scale datasets, which constitute the corresponding dense sampling of diverse real-world scenes, and the renaissance of deep learning techniques, which learn powerful feature representations directly from big raw data, have been bringing remarkable progress in the field of scene representation and classification. To help researchers master needed advances in this field, the goal of this paper is to provide a comprehensive survey of recent achievements in scene classification using deep learning. More than 200 major publications are included in this survey covering different aspects of scene classification, including challenges, benchmark datasets, taxonomy, and quantitative performance comparisons of the reviewed methods. In retrospect of what has been achieved so far, this paper is also concluded with a list of promising research opportunities.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 86 of its 271 references that are in the map. First posted on arXiv 26 Jan 2021.

Builds on

86 of 271 references.

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38 more
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