Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study
Problem
Classifying land use and land cover from high-resolution remote sensing imagery is difficult and time-consuming, and training deep networks from scratch is costly.
Why it matters
Accurate land cover maps underpin agriculture, urban planning, and environmental monitoring decisions.
What the AI did
Fine-tuned pre-trained CNNs (VGG16 and Wide Residual Networks) via transfer learning on the RGB EuroSAT dataset, comparing performance and computational time with optimization techniques.
Results
not stated in source
Impact
Evidence supports a comparative transfer-learning study on a benchmark dataset, not a deployed real-world result.
Record
| Venue | Sensors |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Raoof Naushad, Tarunpreet Kaur, Ebrahim Ghaderpour |
| Citations | 189 (OpenAlex, at import time) |
| References | 48 |
| Model or method | VGG16; Wide Residual Networks (WRNs) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/s21238083 |
| Open access | gold |
| Keywords | transfer learning, CNN, EuroSAT, land use, remote sensing, VGG16 |
Importance rating
real but narrower advance
Abstract claims significant value for high-resolution LULC classification but names no beneficiaries or scale; agriculture, planning, and monitoring benefits are inferred, not stated.
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