Semi-automatic classification for rapid delineation of the geohazard-prone areas using Sentinel-2 satellite imagery

Semi-automatic classification for rapid delineation of the geohazard-prone areas using Sentinel-2 satellite imagery

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Abstract: The study of land use land cover has become increasingly significant with the availability of remote sensing data. The main objective of this study is to delineate geohazard-prone areas using semi-automatic classification technique and Sentinel-2 satellite imagery in Bhutan. An open-source...

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Bibliographic Details
Main Authors: Aryal, K.R (Author), Tempa, K. (Author)
Format: Article
Language:English
Published: Springer Nature 2022
Subjects:
Abstracting
Automatic classification
Automation
Bhutan
Decision trees
Geohazard
Geohazards
Hazards
Image classification
ISODATA
Iterative methods
Land use
Landslides
Open source software
Open systems
Random Forest
Random forest classifier
Random forests
Remote sensing
Satellite imagery
Semi-automatic classification
Semi-automatics
Sentinel-2
Supervised learning
Unsupervised classification
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