MAIG-Net: Unsupervised Remote Sensing Road Extraction Combining Multi-Layer Adversarial Learning and Intermediate Domain Guidance.

Bao C, Chen G, Jin W, Yang J, Li S

DOI: 10.3390/s26165197
Published: 2026 Aug 17
Container: Sensors (Basel, Switzerland)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/s26165197,
  title = {MAIG-Net: Unsupervised Remote Sensing Road Extraction Combining Multi-Layer Adversarial Learning and Intermediate Domain Guidance.},
  author = {Bao C and Chen G and Jin W and Yang J and Li S},
  year = {2026},
  journal = {Sensors (Basel, Switzerland)},
  doi = {10.3390/s26165197},
  url = {https://doi.org/10.3390/s26165197}
}

RIS

TY  - JOUR
TI  - MAIG-Net: Unsupervised Remote Sensing Road Extraction Combining Multi-Layer Adversarial Learning and Intermediate Domain Guidance.
AU  - Bao C
AU  - Chen G
AU  - Jin W
AU  - Yang J
AU  - Li S
PY  - 2026
JO  - Sensors (Basel, Switzerland)
DO  - 10.3390/s26165197
UR  - https://doi.org/10.3390/s26165197
ER  -

APA

C, B., G, C., W, J., J, Y., & S, L. (2026). MAIG-Net: Unsupervised Remote Sensing Road Extraction Combining Multi-Layer Adversarial Learning and Intermediate Domain Guidance.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26165197

Source records

pubmed · retrieved 2026-09-26T17:01:37.489Z