… Recent landslide detection studies have focused on pixel-based deep learning (DL) …
integration framework of a DL model with rule-based object-based image analysis (OBIA) to detect …

… Moreover, feature engineering is avoided in our proposed landslide detection framework
by applying deep learning framework. Therefore, this study can be recognized as an improved …

… With these considerations, the objective of this study is to present the DL based landslide
detection by utilizing freely available open-source resources and also evaluate the model …

… testing of the respective machine learning models 21 . Table 2 Hyperparameters for optimal
values of the machine learning-based … deep neural network (CNN–DNN) to predict landslide …

… , no study focused on deep learning-based methods for landslide detection on hyperspectral
images. We proposes a deep learning framework with constraints to detect landslides on …

… conditions for training and evaluating landslide identification models. In the dataset creation
… deep learning landslide datasets, we demonstrated the advantages of the CAS Landslide …

… instantaneous and exhaustive detection, and further monitoring of landslides. The … machine
learning as well as deep learning applications on remote sensing images to detect landslides…

… In this article, a new deep-learning-based approach was proposed to detect earthquake-triggered
landslides from singletemporal RapidEye imageries. Specifically, data preprocessing …

… inventory serves as the vital role for LSM, its accuracy directly decides the quality and
effectiveness of extracted samples, which will be used for training and testing the machine learning …

… To address these problems, we propose a new lightweight deep-learning-based … machine
learning methods and deeplearning convolutional neural networks for landslide detection…