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Identifying Monuments using Deep Learning
Subject area: Science,Engineering and Technology · Area of research: Tourism
Abstract
This research introduces a web-based platform that leverages machine learning and deep learning techniques to identify and classify heritage monuments from visual data. By utilizing Convolutional Neural Networks (CNNs), the system is capable of analyzing features in satellite and user-contributed images to recognize and label historical sites with high accuracy. The application not only enhances digital documentation of cultural landmarks but also contributes to public engagement by making heritage information easily accessible. The study involves collecting diverse image datasets, applying preprocessing methods, and comparing multiple machine learning algorithms to evaluate classification performance. Through this initiative, we aim to bridge the gap between technology and cultural conservation, offering an open-source tool that fosters awareness and supports the long-term preservation of heritage structures.
Keywords
Heritage Detection, Machine Learning, Deep Learning, Convolutional Neural Network (CNN), Image Classification, Cultural Preservation, Open-Source Application, Pattern Recognition, Satellite Imagery, Indian Monuments, Historical Site Identification, Feature Extraction, Public Engagement, Image Processing, Visual Recognition Systems.
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How to cite this paper
@article{1709121,
author = {Priyanka Panchal, Harsh Rana, Dr. Saroj Kumar Gupta},
title = {Identifying Monuments using Deep Learning},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {12},
pages = {778-786},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709121.pdf},
abstract = {This research introduces a web-based platform that leverages machine learning and deep learning techniques to identify and classify heritage monuments from visual data. By utilizing Convolutional Neural Networks (CNNs), the system is capable of analyzing features in satellite and user-contributed images to recognize and label historical sites with high accuracy. The application not only enhances digital documentation of cultural landmarks but also contributes to public engagement by making heritage information easily accessible. The study involves collecting diverse image datasets, applying preprocessing methods, and comparing multiple machine learning algorithms to evaluate classification performance. Through this initiative, we aim to bridge the gap between technology and cultural conservation, offering an open-source tool that fosters awareness and supports the long-term preservation of heritage structures.},
keywords = {Heritage Detection, Machine Learning, Deep Learning, Convolutional Neural Network (CNN), Image Classification, Cultural Preservation, Open-Source Application, Pattern Recognition, Satellite Imagery, Indian Monuments, Historical Site Identification, Feature Extraction, Public Engagement, Image Processing, Visual Recognition Systems.},
month = {June},
}