Lightweight semantic segmentation for Co-seismic landslide identification using adaptive transfer learning

Autor(en)

Shaoqiang Meng, Zhenming Shi, F. Nex, Saied Pirasteh, Omid Ghorbanzadeh, Thomas Glade

Abstrakt

Rapid and accurate detection of co-seismic landslides is essential for post-earthquake emergency response, yet remains challenging due to scarce labeled data, class imbalance, and overfitting in existing models. To address these issues, the Stepwise Lightweight Adaptive Transfer Learning (SLATL) framework was proposed, a compact semantic segmentation model that balances accuracy and efficiency. The SLATL integrates a Stepwise Lightweight Decoder (SLD) that progressively prunes shallow feature channels to reduce computation, a Unified Spatial–Channel Attention Module (USCAM) that strengthens spatial and channel feature fusion, and a Compact Pyramid Pooling Module (CPPM) that captures multi-scale landslide patterns with minimal overhead. Furthermore, an adaptive transfer learning strategy leverages an intermediate domain to mitigate domain gaps and improve generalization under limited data conditions. Validated on the Hokkaido and Wenchuan datasets, the SLATL achieves mean Intersection-over-Union scores of 0.833 and 0.800, respectively, while requiring only 1.418 Gigaflops (GFLOPs, billions of floating-point operations per second) and 3.578 parameters (in millions, M). These results outperform other lightweight baselines and demonstrate SLATL's potential for rapid and reliable disaster assessment in resource-constrained environments.

Organisation(en)

Institut für Geographie und Regionalforschung

Externe Organisation(en)

Tongji University, University of Twente, Shaoxing University, Universität für Bodenkultur Wien

Journal

Engineering Applications of Artificial Intelligence

Band

166

Anzahl der Seiten

17

ISSN

0952-1976

DOI

https://doi.org/10.1016/j.engappai.2025.113683

Publikationsdatum

12-2025

Peer-reviewed

Ja

ÖFOS 2012

105408 Physische Geographie

Schlagwörter

ASJC Scopus Sachgebiete

Control and Systems Engineering, Electrical and Electronic Engineering, Artificial Intelligence

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/e94047b4-131d-46fa-be1a-968b86c4dd31