September 10, 2025The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesOpen Access

Comparative Analysis of Deep Learning CNN Models and Traditional Machine Learning Approaches for Land Use Land Cover Classification Using Imagery

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Deep learning models, specifically VGG-19, achieved higher accuracy for land use land cover mapping.
The highest Kappa coefficient value observed was 0.94 for VGG-19, indicating strong classification reliability.
Three machine learning and four deep learning models were evaluated using Sentinel 2 imagery for land use classification.
Results highlight the effectiveness of advanced AI techniques in improving urban land planning.

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Implication

This analysis demonstrates deep learning models outperform traditional machine learning in land use classification, indicating their potential for urban sustainability.

Cite This Study

Chaurasia et al. (2025) studied this question.

synapsesocial.com/papers/68c23f1fb210217d64795cd0 https://doi.org/10.5194/isprs-archives-xlviii-g-2025-263-2025

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Implication

This analysis demonstrates deep learning models outperform traditional machine learning in land use classification, indicating their potential for urban sustainability.

Comparative Analysis of Deep Learning CNN Models and Traditional Machine Learning Approaches for Land Use Land Cover Classification Using Imagery

Key Points

Discussion

Implication

Cite This Study

Comparative Analysis of Deep Learning CNN Models and Traditional Machine Learning Approaches for Land Use Land Cover Classification Using Imagery

Key Points

Discussion

Implication

Cite This Study

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Also Consider