Pixel Based Landslide Identification Using Landsat 8 and GEE

Landslide is one of the common natural disasters, triggered mainly due to heavy rainfall, cloud burst, earthquake, unorganized constructions, and deforestation. In India, field surveying is the standard method to identify potential landslide regions and update landslide inventories, but it is costly and inefficient. Alternatively, advanced remote sensing technologies allow rapid and easy data acquisitions and help to improve the traditional method of landslide detection capabilities. For example, machine learning algorithms, Support Vector Machine (SVM), challenge conventional techniques by predicting disasters with reasonable accuracy. In this research work, we have utilized open-source datasets (Landsat 8 images and JAXA ALOS DSM) and Google Earth Engine (GEE) to identify landslide regions in Rudraprayag using machine learning techniques. Labeled landslide locations are obtained from landslide inventory (by Geological Survey of India). The landslide identification has been performed using SVM, Classification and Regression Trees (CART), Minimum Distance, Random forest (RF), and Naive Bayes techniques, in which SVM and RF outperformed all other techniques by achieving an 87.5% true positive rate (TPR).