Transformer-based land use and land cover classification with explainability using satellite imagery
importance 4/10
ai assisted
Remote-Sensing Image Classification land use mapping stage: research mixed
Problem
Transformer models for land use and land cover (LULC) analysis from satellite imagery are too computationally expensive for practical use.
Why it matters
LULC mapping informs forestry, environmental studies, and urban/rural planning decisions.
What the AI did
Fine-tuned transformer models via transfer learning to reduce resource use, with Captum used to explain AI decisions and mitigate bias.
Results
not stated in source
Impact
Evidence supports a proposed cost-accuracy method, but no measured figures appear in the provided abstract.
Record
| Venue | Scientific Reports |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Mehak Khan, Abdul Hanan, Meruyert Kenzhebay, Michele Gazzea, Reza Arghandeh |
| Citations | 66 (OpenAlex, at import time) |
| References | 32 |
| Model or method | Transformer-based models with transfer learning; Captum for explainability |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1038/s41598-024-67186-4 |
| Open access | gold |
| Keywords | transformers, land use classification, satellite imagery, transfer learning, explainability |
Importance rating
4
real but narrower advance
Efficiency improvement for LULC analysis supporting forestry, environmental studies, and planning; a real but narrower computational advance with no stated scale or measured figures.
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