Accurate landslide identification by multisource data fusion analysis with improved feature extraction backbone network

Traditional methods for landslide survey, whether field investigation or human remote sensing-based interpretation approaches, all require considerable labour costs and expert knowledge. Deep learning-based detection methods have significantly improved the speed of landslide recognition, but their accuracy still has much room for improvement. In our work, we propose SA-MFNet to achieve pixelwise landslide detection based on multisource data fusion analysis. On the one hand, we achieve improved feature extraction by utilizing an attention mechanism. On the other hand, based on raw sensing data and labeled results obtained from several regions, we propose a landslide detection model based on the fusion of multisource data, including digital elevation model (DEM), geological mapping data, river distribution data and other data related to earth observation information. We enhance the performance of the developed method via fusion analysis with features extracted from remote optical sensing images, thus achieving precise pixelwise landslide terrain classification and positioning. Experimental results demonstrate that the model proposed in this article is superior to the existing common baselines and can provide technical support for automatic landslide identification with practical value.