Objectives Earthquake-induced mass landslides are a severe type of secondary disaster following earthquakes, causing significant casualties and substantial economic losses. Rapidly and accurately identifying these earthquake-induced landslides after an event is crucial for national emergency departments to assess disaster severity and formulate relief measures. Although synthetic aperture radar (SAR) offers all-time, all-weather imaging capabilities, its effectiveness in identifying landslides is currently limited due to the complexity of the background and the less prominent features of landslides in SAR images.

Methods This paper proposes a siamese network for landslide identification in SAR images, named the difference and aggregated feature cross fusion siamese network (DACS-Net). This network, composed of an encoder-decoder structure, utilizes a siamese feature extraction network in the encoder to extract features of pre- and post-earthquake SAR images at different resolutions. In the decoder, multi-scale difference feature and aggregated feature generation modules are constructed to perform difference extraction and feature aggregation for landslide feature maps at different scales, fully representing the characteristics of earthquake-induced landslides. The proposed multi-feature cross-fusion module densely connects and decodes different scales of aggregated and difference features layer by layer, enhancing the extraction of detail and semantic features, leading to the identification results.

Results Experiment utilized Sentinel-1 data on earthquake-triggered landslides in Papua New Guinea and Milin, results show that the proposed method could effectively identify earthquake-induced landslides. The landslide identification precision could reached 70.75% and 76.52%, recall were 60.92% and 71.20%, F1 score were 65.46% and 74.03%, and overall accuracy were 91.00% and 86.14%.

Conclusion By utilizing this network, achievements have been made in the recognition of landslides in SAR images, enhancing the practical application value of deep learning networks in landslide detection. This holds significant importance for disaster emergency response.