E-DeepLabV3+: A Landslide Detection Method for Remote Sensing Images

Landslides are common hazardous geological events, and accurate and efficient landslide identification methods are important for hazard assessment and post-disaster response to geological disasters. Deep learning (DL) methods based on remote sensing data are currently widely used in landslide identification tasks. The recently proposed segment anything model (SAM) has shown strong generalization capabilities in zero-shot semantic segmentation. Nevertheless, SAM heavily relies on user-provided prompts, and performs poorly in identifying landslides on remote sensing images. In this study, we propose a SAM-based cross-feature fusion network (SAM-CFFNet) for the landslide identification task. The model utilizes SAM’s image encoder to extract multi-level features and our proposed cross-feature fusion decoder (CFFD) to generate high-precision segmentation results. The CFFD enhances landslide information through fine-tuning and cross-fusing multi-level features while leveraging a shallow feature extractor (SFE) to supplement texture details and improve recognition performance. SAM-CFFNet achieves high-precision landslide identification without the need for prompts while retaining SAM’s robust feature extraction capabilities. Experimental results on three open-source landslide datasets show that SAM-CFFNet outperformed other comparative models in terms of landslide identification accuracy and achieved an intersection over union (IoU) of 77.13%, 55.26%, and 73.87% on the three datasets, respectively. Our ablation studies confirm the effectiveness of each module designed in our model. Moreover, we validated the justification for our CFFD design through comparative analysis with diverse decoders. SAM-CFFNet achieves precise landslide identification using remote sensing images, demonstrating the potential application of the SAM-based model in geohazard analysis.

Section: Results Of Comparative Experimentsmentioning

confidence: 99%

SAM-CFFNet: SAM-Based Cross-Feature Fusion Network for Intelligent Identification of Landslides

Xi,

Yu,

et al. 2024

“…It plays an increasingly important role in disaster prevention and mitigation applications. After reviewing the currently available literature on landslide recognition in optical remote sensing images, we found that several classic models such as ResNet [24,25], YOLO [26][27][28][29][30], Mask R-CNN [31][32][33][34], U-Net [28,[35][36][37], DeeplabV3+ [38][39][40], Transformer [41][42][43], and EfficientNet [44,45] and several open landslide datasets such as Bijie landslide dataset [24], HR-GLDD dataset [46], CAS Landslide Dataset [47], and so on, have been popularly used for landslide recognition. In this paper, we will first introduce the fundamentals of landslide recognition based on deep learning and then discuss and analyze the current development status of each type of model; finally, we will compare the advantages and disadvantages of each model and analyze the development trends of landslide identification.…”

Section: Introductionmentioning

confidence: 99%

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

Cheng,

Wang,

Huang

et al. 2024

Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy. This article first provides an overview of deep learning technology and its basic principles, as well as the current status of landslide remote sensing databases. Then, classic landslide deep learning recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet, DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were extensively analyzed. Finally, the current constraints of deep learning in landslide identification were summarized, and the development direction of deep learning in landslide identification was analyzed. The purpose of this article is to promote the in-depth development of landslide identification research in order to provide academic references for the prevention and mitigation of landslide disasters and post-disaster rescue work. The research results indicate that deep learning methods have the characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention should be paid to the development of emerging deep learning models in landslide recognition in the future.

Enhanced Dual-Channel Model-Based with Improved Unet++ Network for Landslide Monitoring and Region Extraction in Remote Sensing Images

Wang,

Zhang,

Xie

et al. 2024

Landslide disasters pose significant threats to human life and property; therefore, accurate and effective detection and area extraction methods are crucial in environmental monitoring and disaster management. In our study, we address the critical tasks of landslide detection and area extraction in remote sensing images using advanced deep learning techniques. For landslide detection, we propose an enhanced dual-channel model that leverages EfficientNetB7 for feature extraction and incorporates spatial attention mechanisms (SAMs) to enhance important features. Additionally, we utilize a deep separable convolutional neural network with a Transformers module for feature extraction from digital elevation data (DEM). The extracted features are then fused using a variational autoencoder (VAE) to mine potential features and produce final classification results. Experimental results demonstrate impressive accuracy rates of 98.92% on the Bijie City landslide dataset and 94.70% on the Landslide4Sense dataset. For landslide area extraction, we enhance the traditional Unet++ architecture by incorporating Dilated Convolution to expand the receptive field and enable multi-scale feature extraction. We further integrate the Transformer and Convolutional Block Attention Module to enhance feature focus and introduce multi-task learning, including segmentation and edge detection tasks, to efficiently extract and refine landslide areas. Additionally, conditional random fields (CRFs) are applied for post-processing to refine segmentation boundaries. Comparative analysis demonstrates the superior performance of our proposed model over traditional segmentation models such as Unet, Fully Convolutional Network (FCN), and Segnet, as evidenced by improved metrics: IoU of 0.8631, Dice coefficient of 0.9265, overall accuracy (OA) of 91.53%, and Cohen’s kappa coefficient of 0.9185 on the Bijie City landslide dataset; and IoU of 0.8217, Dice coefficient of 0.9021, overall accuracy (OA) of 96.68%, and Cohen’s kappa coefficient of 0.8835 on the Landslide4Sense dataset. These findings highlight the effectiveness and robustness of our proposed methodologies in addressing critical challenges in landslide detection and area extraction tasks, with significant implications for enhancing disaster management and risk assessment efforts in remote sensing applications.