A multi-modal deep learning framework with GAN-based fusion for enhanced landslide detection

R Srivats, Deepika Roselind Johnson, G Logeswari, R Saimirra, Muskaan Siddiqui and Abhiram Sharma

PLOS ONE, 2026, vol. 21, issue 4, 1-42

Abstract: The proposed work presents a hybrid deep learning framework that integrates four pre-trained Convolutional Neural Networks that include VGG16, DenseNet201, ResNet50 and InceptionV3. The pre-trained CNNs are combined with a GAN-based adversarial refinement module for accurate landslide detection and segmentation. Unlike traditional single-CNN or ensemble models, the proposed model performs multi-backbone feature fusion to accurately capture global level terrain context and fine-grained spatial details. The GAN component sharpens boundaries and suppresses noisy predictions through discriminator-guided refinement. The proposed system generates GIS-ready probability maps with confidence layers. They are also optimized for low-latency inference, making it suitable for rapid post-disaster decision support. The proposed work is evaluated on three benchmark datasets - CAS Landslide (high-resolution GF-2/UAV imagery), MS2LandsNet (medium-resolution Sentinel-2) and GDCLD (coseismic landslides). The proposed framework achieves F1-scores of 97.24%, 93.70% and 94.75% across the three datasets. These results correspond to improvements of 1.4 to 2.9% over fusion baselines and 4–7% over single-CNN models such as VGG16, DenseNet201,ResNet50 andInceptionV3. The results highlight consistent IoU gains and improved boundary delineation. The cross-dataset experiments further demonstrate strong generalization across varying resolutions, terrain types and triggering mechanisms. To our knowledge, this is the first landslide segmentation study to combine multi-backbone feature fusion with adversarial mask refinement in an operational monitoring context. The results confirm that the proposed framework delivers high accuracy, scalability and deployment readiness making.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0347324 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 47324&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0347324

DOI: 10.1371/journal.pone.0347324

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().