Penta Classification of Landslide Via Deep Learning Based SegNet and RegNet

Landslides are one of the most frequent and destructive natural disasters, posing severe threats to human lives, infrastructure, and the environment. Accurate prediction and classification of landslide occurrences using remote sensing and aerial imagery have become vital for disaster management and mitigation. However, existing deep learning models often fail to capture fine-grained spatial variations and multi-class distinctions leads to inaccurate segmentation and misclassification of landslide-affected regions. To overcome these challenges, a novel penta classification of landslide via deep learning based SegNet and RegNet (PENTA-SERE) model has been proposed. Initially, the 180 aerial images from INRIA Aerial Image Labelling Dataset were collected for landslides classification. The input aerial images are denoised using adaptive bilateral filter (ABF) to reduce the noise artifacts. The denoised images are fed into the SegNet to identify and separate the regions corresponding to different landslide areas. Then, the segmented regions are extracted using RegNet for efficient feature representation and enhanced discrimination among different types of landslides. Finally, the Deep Belief Network (DBN) is used for penta classification such as Rock falls, Debris flaws, Creep, Solifluction and earth slides. The performance analysis of the proposed PENTA-SERE model is evaluated in terms of accuracy, specificity, precision, recall and F1score based on the INRIA Aerial Image Labelling Dataset. The proposed PENTA-SERE model achieves an overall accuracy of 99.29%, and F1 Score of 96.62% based on the gathered dataset. The Proposed PENTA-SERE model improves the overall accuracy by 17.41, 27.45, 6.83, and 2.44% better than Bidirectional Encoder Representations from Transformers (BERT), Support Vector Machine (SVM), Deep convolutional neural network (DCNN), InceptionV3 respectively.