Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/443936
Title: | Hurricane Damage Detection from Satellite Imagery using Deep Learning |
Researcher: | Swapandeep Kaur |
Guide(s): | Sheifali Gupta and Swati Singh |
Keywords: | Engineering Engineering and Technology Engineering Electrical and Electronic |
University: | Chitkara University, Punjab |
Completed Date: | 2022 |
Abstract: | Hurricanes or tropical cyclones are one of the most calamitous natural disasters occurring newlineon earth. They are accompanied by heavy rainfall, floods, and very high-speed winds at the newlinerate of 200 miles/hour causing excessive damage to property and human casualties. When newlinea hurricane occurs, it is essential to assess the damage for providing relief instantly to the newlineaffected people. The extent of damage could be found by analyzing flooded /damaged newlinebuildings. Earlier this was done through a ground survey which was a tedious task. newlineImmediate steps need to be taken and social media platforms like Twitter help to provide newlinerelief to the affected public. However, it is difficult to analyze high-volume data obtained newlinefrom social media posts. Therefore the efficiency and accuracy of useful data extracted newlinefrom the enormous posts related to disasters are low. newline |
Pagination: | |
URI: | http://hdl.handle.net/10603/443936 |
Appears in Departments: | Faculty of Electronics |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
80_recommendation.pdf | Attached File | 141.75 kB | Adobe PDF | View/Open |
abstract.pdf | 91.23 kB | Adobe PDF | View/Open | |
annexures.pdf | 167.02 kB | Adobe PDF | View/Open | |
chapter 1.pdf | 770.61 kB | Adobe PDF | View/Open | |
chapter 2.pdf | 219.29 kB | Adobe PDF | View/Open | |
chapter 3.pdf | 337.85 kB | Adobe PDF | View/Open | |
chapter 4.pdf | 1.82 MB | Adobe PDF | View/Open | |
chapter 5.pdf | 785.91 kB | Adobe PDF | View/Open | |
chapter 6.pdf | 1.3 MB | Adobe PDF | View/Open | |
chapter 7.pdf | 379.22 kB | Adobe PDF | View/Open | |
contents.pdf | 61.28 kB | Adobe PDF | View/Open | |
preliminary pages.pdf | 236.01 kB | Adobe PDF | View/Open | |
title.pdf | 9.22 kB | Adobe PDF | View/Open |
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