ViTs and CNNs Compared for Land Use Scene Classification

Researchers have compared the effectiveness of Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) for classifying land use scenes from remote sensing imagery. The study utilized benchmark datasets like UC Merced Land Use and EuroSAT, evaluating metrics such as accuracy, precision, and recall. Findings indicate that CNNs are more robust with limited data and strong local features, while ViTs excel at understanding global spatial relationships when ample training data is available, though they require more computational resources. AI

IMPACT Provides guidance on selecting appropriate deep learning models for remote sensing land use classification tasks.

RANK_REASON This is a research paper comparing two existing architectures for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

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• Arun Kulkarni
• EuroSAT Land Use dataset
• Land Use Scene Classification
• UC Merced Land Use dataset
• Vision Transformers

• paper
• other

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