Advances in Deep Learning Models for Ground Deformation Prediction Using InSAR Approach: A Systematic Review with Special Emphasis on Subsidence as a Geo-Environmental Hazard.
- DOI
- 10.3390/s26196358
- Published
- 2026 Oct 8
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s26196358,
title = {Advances in Deep Learning Models for Ground Deformation Prediction Using InSAR Approach: A Systematic Review with Special Emphasis on Subsidence as a Geo-Environmental Hazard.},
author = {Kumar N and Alam MS},
year = {2026},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s26196358},
url = {https://doi.org/10.3390/s26196358}
}RIS
TY - JOUR TI - Advances in Deep Learning Models for Ground Deformation Prediction Using InSAR Approach: A Systematic Review with Special Emphasis on Subsidence as a Geo-Environmental Hazard. AU - Kumar N AU - Alam MS PY - 2026 JO - Sensors (Basel, Switzerland) DO - 10.3390/s26196358 UR - https://doi.org/10.3390/s26196358 ER -
APA
N, K., & MS, A. (2026). Advances in Deep Learning Models for Ground Deformation Prediction Using InSAR Approach: A Systematic Review with Special Emphasis on Subsidence as a Geo-Environmental Hazard.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26196358
Source records
- pubmed · retrieved 2026-11-10T06:56:10.429Z