Avalanche Debris Detection From Sentinel-2 Data Using Fuzzy Machine Learning And Colour Spaces For The Indian Himalaya
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https://doi.org/10.1080/2150704X.2025.2488532 <-- shared paper
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#GIS #spatial #mapping #snowavalanches #snow #avalanches #machinelearning #fuzzyclassification #SVM #AI #randomforest #model #modeling #forecasting #risk #hazard #massmovement #engineeringgeology #remotesensing #earthobservation #imagery #spatialanalysis #spatiotemporal #change #debris #detection #satellite #sentinel #Himalaya #Himalayas #performance
![work flow graphic - Phenology method flowchart. This flowchart is the overview of capturing the phenology training data for cheatgrass (BRTE [44]) and medusahead (TACA8)
(a) The methods for extracting high probability BRTE and TACA8 pixels
(b) the decision tree analysis development using exotic annual grass (EAG) training data from Benedict et al.
(c) developing the training data
(d) developing the phenology model.](https://files.techhub.social/media_attachments/files/113/556/787/746/346/628/original/181cfc9bb3710e94.jpg "work flow graphic - Phenology method flowchart. This flowchart is the overview of capturing the phenology training data for cheatgrass (BRTE [44]) and medusahead (TACA8)
(a) The methods for extracting high probability BRTE and TACA8 pixels
(b) the decision tree analysis development using exotic annual grass (EAG) training data from Benedict et al.
(c) developing the training data
(d) developing the phenology model.")
