Landslide Recognition Based on Bisenet Lightweight Network

Prompt and accurate identification of landslides immediately after a disaster is crucial for evaluating the subsequent hazards and risks in the affected areas. The proposed method in this paper aims to detect landslides and accurately determine their extent using semantic segmentation. Many existing models rely on multi-temporal or geological data to enhance their accuracy. However, the inclusion of a large amount of data introduces additional parameters, consumes significant computational resources, and requires lengthy training time. To address these challenges, this paper introduces a lightweight network called BiSeNet for landslide recognition. By incorporating modifications to the network’s skip connections, dense connections are added to BiSeNet. This modification aims to improve the network’s performance in landslide detection while minimizing computational requirements and training time. Experimental results revealed that the BiSeNet-DenseNet model achieved an F1-score value of 0.8856, showing a significant 30.35% improvement over BiSeNet-Xception.