Machine Learning Based Remote Sensing Technique for Analysis of The Glaciated Regions

Autor: Chandel Garima, Sahimkhan Pathan, Verma Saweta, Sharm Ashish
Jazyk: English<br />French
Rok vydání: 2023
Předmět:
Zdroj: E3S Web of Conferences, Vol 405, p 02019 (2023)
Druh dokumentu: article
ISSN: 2267-1242
DOI: 10.1051/e3sconf/202340502019
Popis: Remote Sensing has become one of the most developed technologies in the world. Its applications are wide, like it can be used in agriculture, disaster observing, water resources monitoring, environment, marine resources, forestry as well as the forest fire, coastal zone snow and glacier etc. Machine learning applications like visualisation of data are used for understanding the remote sensing data graphically. In this paper presents the method for the process of representing the remote sensing data on glaciers graphically and pictorially. The matplotlib and seaborn libraries in python are used for this process. Python is the easy programming language used for visualisation of data with its libraries NumPy, pandas, matplotlib, seaborn and plotly. These libraries are used in python for representing the data graphically. In this work, the benchmark WGI dataset on remote sensing of glaciers covered with the debris has been used. Machine learning algorithms has been proposed for classification of the glaciers that are covered with the debris.
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