Landslides constitute one of the most destructive geological hazards worldwide,causing thousands of casualties and billions in economic losses annually.To mitigate these risks,accurate and efficient pixel-wise mapping...Landslides constitute one of the most destructive geological hazards worldwide,causing thousands of casualties and billions in economic losses annually.To mitigate these risks,accurate and efficient pixel-wise mapping of landslides for automatic semantic segmentation is of paramount importance.While recent advances in deep learning,particularly with transformer architectures and large pre-trained models like the Segment Anything Model(SAM),have shown promise,their application to landslide mapping is often hindered by high compu-tational costs,prompt dependency,and challenges with data imbalance.To address these limitations,we propose GeoNeXt,a novel semantic segmentation architecture for intelligent landslide recognition.It combines a scalable,pre-trained ConvNeXt V2 encoder with a decoder that utilizes Pyramid Squeeze Attention(PSA)and Atrous Spatial Pyramid Pooling(ASPP)to capture multi-scale features.Through domain adaptation on the large-scale CAS landslide dataset,we refined the encoder’s general pre-trained features to learn robust,landslide-specific features.GeoNeXt exhibited zero-shot transferability,achieving 74-78%F1 and 64-66%mIoU across three distinct test datasets from diverse regions,which were entirely excluded from the training process.Ablation studies on decoder variants validated the PSA-ASPP synergy,achieving a superior F1 of 90.39%and mIoU of 83.18%on the CAS dataset.Comparative analysis confirmed that GeoNeXt outperformed SAM-based methods,achieving F1 scores of 94.25%,86.43%,and 92.27%(mIoU:89.51%,78.21%,86.02%)on the Bijie,Landslide4Sense,and GVLM datasets,respectively,with 10×fewer parameters than SAM-based methods and lower computational demands.We showed that modernized convolutions,paired with strategic training,were a viable alternative to resource-intensive transformers.This efficiency facilitated their use in operational intelli-gent landslide recognition and geohazard monitoring systems.展开更多
文摘Landslides constitute one of the most destructive geological hazards worldwide,causing thousands of casualties and billions in economic losses annually.To mitigate these risks,accurate and efficient pixel-wise mapping of landslides for automatic semantic segmentation is of paramount importance.While recent advances in deep learning,particularly with transformer architectures and large pre-trained models like the Segment Anything Model(SAM),have shown promise,their application to landslide mapping is often hindered by high compu-tational costs,prompt dependency,and challenges with data imbalance.To address these limitations,we propose GeoNeXt,a novel semantic segmentation architecture for intelligent landslide recognition.It combines a scalable,pre-trained ConvNeXt V2 encoder with a decoder that utilizes Pyramid Squeeze Attention(PSA)and Atrous Spatial Pyramid Pooling(ASPP)to capture multi-scale features.Through domain adaptation on the large-scale CAS landslide dataset,we refined the encoder’s general pre-trained features to learn robust,landslide-specific features.GeoNeXt exhibited zero-shot transferability,achieving 74-78%F1 and 64-66%mIoU across three distinct test datasets from diverse regions,which were entirely excluded from the training process.Ablation studies on decoder variants validated the PSA-ASPP synergy,achieving a superior F1 of 90.39%and mIoU of 83.18%on the CAS dataset.Comparative analysis confirmed that GeoNeXt outperformed SAM-based methods,achieving F1 scores of 94.25%,86.43%,and 92.27%(mIoU:89.51%,78.21%,86.02%)on the Bijie,Landslide4Sense,and GVLM datasets,respectively,with 10×fewer parameters than SAM-based methods and lower computational demands.We showed that modernized convolutions,paired with strategic training,were a viable alternative to resource-intensive transformers.This efficiency facilitated their use in operational intelli-gent landslide recognition and geohazard monitoring systems.
