MultiResUNet, VGG16, and U-Net applications for landslide detection

Abstract

The frequent occurrence of disastrous landslides can lead to significant infrastructure damages, loss of life, and the relocation of populations. Early detection of landslides is crucial for mitigating the consequences. Today, deep learning algorithms, particularly fully convolutional networks (FCNs) and their variants such as the ResU-Net, have been utilized to rapidly and automatically detecting landslides. In the current study, a novel method using three new deep learning models: MultiResUNet, VGG16, and U-Net was used to detect landslides in Hokkaido Island, Japan. Our dataset is comprised of Sentinel-2 images and a mask layer, which includes "landslide" or "non-landslide" labels. The suggested framework was based on the analysis of satellite images of landslide-prone locations using bands 2 (blue), 3 (green), 4 (red), and 5 (visible and near-infrared) of Sentinel 2, slope and elevation factors. We trained each model on the dataset and evaluated their performance using a variety of statistical indexes, including precision, recall, and F1 score. The results showed that the MultiResUNet model outperformed the other two models, achieving an accuracy of 82.7%. The VGG16 and U-Net models achieved accuracies of 65.5% and 67.2%, respectively. The results indicated the capability of deep learning algorithms to process satellite images for early landslide detection and provide the opportunity of implementing efficient and effective disaster management strategies.