Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery

Muhammad Fayaz, Junyoung Nam, L. Minh Dang, Hyoung‐Kyu Song, Hyeonjoon Moon

Applied Sciences 2024 article cited by 78

importance 4/10 ai assisted Remote-Sensing Image Classification remote sensing / urban planning stage: research open-source model

Traditional remote-sensing methods cannot precisely classify dynamic and complex urban land areas.

Accurate land-use classification informs urban planning, agricultural zoning, and environmental monitoring.

Transfer learning with Inception-v3 and DenseNet121 CNN architectures classifies urban land use from high-resolution satellite imagery, leveraging pre-trained features.

not stated in source

Abstract claims a reliable land-use classification system; accuracy and adoption not evidenced.

Venue Applied Sciences
Published 2024
Work type article
Authors Muhammad Fayaz, Junyoung Nam, L. Minh Dang, Hyoung‐Kyu Song, Hyeonjoon Moon
Citations 78 (OpenAlex, at import time)
References 40
Model or method Inception-v3, DenseNet121 (transfer learning)
Organization not stated in source
Primary source https://doi.org/10.3390/app14051844
Open access gold
Keywords deep learning, transfer learning, remote sensing, CNN, urban land use
4 / 10

real but narrower advance

Accurate urban land-use classification supports urban planning, agricultural zoning, and environmental monitoring, but abstract provides no scale or measured adoption; a real but narrower methodological advance.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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