| Issue |
E3S Web of Conf.
Volume 405, 2023
2023 International Conference on Sustainable Technologies in Civil and Environmental Engineering (ICSTCE 2023)
|
|
|---|---|---|
| Article Number | 02019 | |
| Number of page(s) | 9 | |
| Section | Renewable Energy & Electrical Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202340502019 | |
| Published online | 26 July 2023 | |
Machine Learning Based Remote Sensing Technique for Analysis of The Glaciated Regions
1 Department of Electronics & Communication Engineering, Chandigarh University Mohali, India
2 Department of Mechatronics, Chandigarh University Mohali, India
3 Department of Computer Science, Chandigarh University Mohali, India
4 Department of Electrical Engineering, Chandigarh University Mohali, India
* Corresponding author: chandelgarima5@gmail.com
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.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.