Multi-Branch Deep Learning Framework for Land Scene Classification in Satellite Imagery
Problem
Automatically classifying land scenes in high-resolution satellite imagery is difficult due to complex textures, cluttered backgrounds, small objects and large scale variations.
Why it matters
Accurate land scene classification underpins environmental monitoring, urban planning and Earth observation.
What the AI did
A multi-branch deep learning framework combines global contextual features with multi-scale features to identify complex land scenes.
Results
not stated in source
Impact
Proposes a framework to address known classification challenges; abstract gives no measured performance.
Record
| Venue | Remote Sensing |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Sultan Daud Khan, Saleh M. Basalamah |
| Citations | 72 (OpenAlex, at import time) |
| References | 93 |
| Model or method | multi-branch deep learning framework (no further name given) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/rs15133408 |
| Open access | gold |
| Keywords | deep learning, satellite imagery, scene classification, remote sensing, multi-scale features |
Importance rating
real but narrower advance
Land scene classification supports environmental monitoring, Earth observation, and urban planning, per abstract, but no scale or deployment evidence; a methodological advance on benchmark datasets.
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