  Landslides inflict substantial societal and economic damage, underscoring
their global significance as recurrent and destructive natural disasters.
Recent landslides in northern parts of India and Nepal have caused significant
disruption, damaging infrastructure and posing threats to local communities.
Convolutional Neural Networks (CNNs), a type of deep learning technique, have
shown remarkable success in image processing. Because of their sophisticated
architectures, advanced CNN-based models perform better in landslide detection
than conventional algorithms. The purpose of this work is to investigate CNNs'
potential in more detail, with an emphasis on comparison of CNN based models
for better landslide detection. We compared four traditional semantic
segmentation models (U-Net, LinkNet, PSPNet, and FPN) and utilized the ResNet50
backbone encoder to implement them. Moreover, we have experimented with the
hyperparameters such as learning rates, batch sizes, and regularization
techniques to fine-tune the models. We have computed the confusion matrix for
each model and used performance metrics including precision, recall and
f1-score to evaluate and compare the deep learning models. According to the
experimental results, LinkNet gave the best results among the four models
having an Accuracy of 97.49% and a F1-score of 85.7% (with 84.49% precision,
87.07% recall). We have also presented a comprehensive comparison of all
pixel-wise confusion matrix results and the time taken to train each model.
