Ure 2. Workflow2. Workflow of resampling multispectral photos, NDVI, and slope factor; Preparing and methodology for landslide detection NDVI, and FigurePreparing plus the resampling multispectral pictures,utilizing CAE. slope aspect; Preparing and methodology for landslide detection NDVI, reduction; two. Workflow from the resampling multispectral for dimensionality slope aspect; Applying MNF on multispectral pictures dimensionality and FigureApplying MNF on multispectral photos for images,making use of CAE. reduction; Preparing and methodology for landslide detection NDVI, and Applying MNF on multispectral images for dimensionality MNF; two. Workflow slope aspect and NDVI with resulting options fromslope issue; CAY10502 References Stacking of aspect and NDVI with resulting features from the the MNF; FigureStacking slope the resampling multispectral images,utilizing CAE. reduction; Preparing the on multispectral Applying and methodology for Stacking ofwithfactor and NDVI photos for dimensionality FigureFeeding CAEMNF stacked data; landslide detection making use of CAE. reduction; 2. WorkflowCAE with stacked multispectral pictures, NDVI, fromslope element; Feeding slope resampling data; with resulting functions plus the MNF;Remote Sens. 2021, 13, x FOR PEER Overview Preparing and resampling multispectral Applying MNF on multispectral imagesPreparing CAE deep capabilities applying resulting attributes and Applying and element and NDVI Stacking slope resampling applying mini-batch K-means; along with the Feeding CAE with multispectral photos for dimensionality reduction; Clustering CAE deep attributes multispectral images,K-means; and MNF; ClusteringMNF on stacked information; withmini-batch NDVI, fromslope issue; 7 of 29 pictures, NDVI, and for dimensionality the many Stacking slope issue and NDVIfor landslide features andreduction; Evaluating clustering benefits for landslideresultingdetectionfromslope element; assessment Feeding CAE with stacked information; withmini-batch by means of a variety of accuracy accuracy Clustering CAE deep capabilities utilizing detection K-means;throughMNF; Evaluating clustering outcomes Applying MNF on stacked data; withmini-batch K-means;throughMNF; Stacking slope factor attributes utilizing resulting characteristics and Penicolinate A MedChemExpress metrics. Feeding CAE with multispectral photos for dimensionality reduction; Clustering CAE deepand NDVIfor landslide detectionfrom the several accuracy Evaluating clustering final results assessment metrics. Stacking slope factor functions utilizing resulting options and Feeding CAE with stacked information; withmini-batch K-means;by means of many accuracy Clustering CAE deepand NDVIfor landslide detectionfrom the MNF; Evaluating clustering final results assessment metrics. Feeding Datasets CAE with stacked information; Evaluating clustering benefits for mini-batch K-means; and three.2. Clustering CAE deep capabilities usinglandslide detection through numerous accuracy assessment metrics. Datasets Evaluating clustering benefits for mini-batch K-means; and three.2. Clustering CAE deep capabilities usinglandslide detection through several accuracy 3.2.1.assessment metrics. Sentinel-2A Information Evaluating clustering final results for landslide detection by means of a variety of accuracy three.two. Datasets 3.two.1.assessment metrics. Sentinel-2A Information In this study, Sentinel-2A multispectral images were utilised for landslide detection. three.two. Datasets 3.2.1.assessment metrics. dates for study places in India, China,for landslide detection. Sentinel-2A Information The In this study, Sentinel-2A multispectral images have been made use of and Taiwan were 14 image acquisition 3.2. Datasets three.two.1.Within this 2018.