Year: 2025
Type: article
Abstract: Natural scene classification is a critical computer vision task that has shown significant advancements with the integration of Deep Learning algorithms.Traditionally, recognizing and categorizing different natural environments, such as forests, beaches, or mountains, present challenges due to the complexity and variability of natural imagery.Deep Learning has undergone a revolution with the introduction of Convolutional Neural Networks.By automatic extraction of hierarchical features from unprocessed image data, these models have surpassed traditional methods in accuracy and efficiency.These deep-learning models can capture subtle patterns and textures inherent in natural scenes, leading to more precise classifications.The current study consists of a thorough analysis of previous research on Deep Learning methods for classifying natural scenes.The publicly accessible information and data-gathering methods are used here, beginning with Web of Science, Elsevier, Springer, Scopus, IEEE Xplore, and Google Scholar, wherein hundreds of publications were selected to start the review process.Next, using a thorough screening process, seventy papers are finalized and selected for detailed literature review.The work evaluates many scene classifications based on various performance criteria.In the end, the review also paves the path for future directions for exploration in the scene classification field.
Authors: Priti Priti, Dinesh Singh, Savita Ahlawat
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414816456
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