Efficient Deep Semantic Segmentation for Land Cover Classification Using Sentinel Imagery
importance 4/10
ai assisted
Remote-Sensing Image Classification remote sensing / geospatial stage: research
Problem
Land cover classification is hindered by time-consuming annotation, imagery download, and pre-processing tasks that discourage policy makers from creating and using new land cover maps.
Why it matters
Land cover maps inform policy and decision making.
What the AI did
The authors modified an efficient deep semantic segmentation approach (U-TAE) to use a single multiband image composite as input, combined with Google Earth Engine cloud processing.
Results
not stated in source
Impact
Claims to greatly facilitate land cover classification; impact not quantified in the abstract.
Record
| Venue | Remote Sensing |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Anastasios Tzepkenlis, Konstantinos Marthoglou, Nikos Grammalidis |
| Citations | 55 (OpenAlex, at import time) |
| References | 61 |
| Model or method | Modified U-TAE semantic segmentation model |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/rs15082027 |
| Open access | gold |
| Keywords | land cover, semantic segmentation, Sentinel imagery, Google Earth Engine, remote sensing |
Importance rating
4
real but narrower advance
Eases land cover map creation for policy makers; abstract claims facilitation but provides no scale of use, users, or beneficiaries.
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