aceModerate evidence
Published recordScienceGood Space
More
Deep learning approaches on Landsat observations: a review and meta-analysis
- Truvace record
- TRV-2026-2717raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
- sha256
- 764bfd2b9980…check it
Although multiple summary reviews of deep learning in remote sensing have been conducted, the field currently lacks a comprehensive analysis of deep learning utilizing Landsat observations in single time pixel-based classification, both qualitatively and quantitatively. This study addresses this gap by conducting a systematic review based on 352 case studies extracted from 279 peer-reviewed articles that supported direct comparisons across deep learning and traditional classifiers. Qualitatively, our findings ar…
The gain
A systematic review and meta-analysis of 352 case studies from 279 peer-reviewed papers found deep neural networks improved Landsat pixel classification accuracy by about 5% when traditional classifiers scored 80%.
What this doesn’t fix
UNets need labels for large contiguous reference patches, and Transformers need more computing resources and memory, which limits where their accuracy gains can be applied; most studies also focused on single countries.
Sources
1 on file- International Journal of Remote Sensingdoi:10.1080/01431161.2025.2520050
Cite this record
v1Truvace Impact Record TRV-2026-2717, v1: “Deep learning approaches on Landsat observations: a review and meta-analysis.” Truvace, 2026-11-12. /record/TRV-2026-2717 (accessed on the day you copy it). sha256 764bfd2b9980abdf…
Change history
1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
Certified into the record
Check this record hasn’t been altered
How to check it yourself, without trusting this page
Download any version from /api/record/TRV-2026-2717 and hash its canonical field yourself, for example with shasum -a 256. You should get its content_hash. Each version’s text also ends with prev:and the hash of the version before it (version 1 points to 64 zeros). If anyone had changed a single character of any version after it was saved, those hashes wouldn’t line up.