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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM32987

Continent Based Geo-Landmark Detection and Recognition Using CNN

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM32987

Volume:

8 |

4 |

Apr 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM32987

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PPD

Prof. Priyanka Deshmane

SP

Suyog Palav

TP

Tejas Pichad

SS

Sanket Shelar

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Abstract

In the modern world, we need the development of an automated system that can accurately detect landmarks and provide essential services is important. The ‘Continent-based Geo-Landmark Detection and Recognition using CNN” project develop the Continent-based Landmark Detection system using deep learning, specifically Convolutional neural network (CNNs). The system will be capable of identifying landmarks from uploaded images. It will provide a short summary about the identified landmarks, and extract geographic data such as landmark address, latitude and longitude and display them on the map. Additionally, the system will utilize location information (i.e. Latitude and Longitude) to locate 17 essential services such as Road Network, Hospitals, Bus Stops, Railway Station, ATM’s, Religious Institutions (Temple, Masjid, Church and more) etc. around the identified landmark within a specified radius, such as 1000 meters (i.e. 1 kilometer).

KEYWORDS

CNN Continents Geo-landmark Detection and Recognition Convolution neural network Deep Learning Python.

Cite This Article

Prof. Priyanka Deshmane, Suyog Palav, Tejas Pichad, Sanket Shelar, "Continent Based Geo-Landmark Detection and Recognition Using CNN," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 4, Apr 2024. https://doi.org/10.55041/IJSREM32987

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Publication Date

12/31/45413

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Review Rounds: 2

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