Abstract
The archeological sites are a heritage that we have gained from our ancestors. These sites are crucial for understanding the past and the way of life of people during those times. The monuments and the immovable relics of ancient times are a getaway to the past. The critical cultural relics however actually over the years have faced the brunt of nature. The environmental conditions have deteriorated the condition of many important immovable relics over the years since these could not be just shifted away. People also move around the ancient cultural relics that may also deform these relics. The machine learning algorithms were used to identify the location of the relics. The data from the satellite images were used and implemented machine learning algorithm to maintain and monitor the relics. This research study dwells into the importance of the area from a research point of view and utilizes machine learning techniques called CaffeNet and deep convolutional neural network. The result showed that 96% accuracy of predicting the image, which can be used for tracking human activity, protects heritage sites in a unique way.
Highlights
The following study presents the challenges in modern times the heritages face due to the encroached human activity and the environmental conditions
The identification process has been associated with the human activity of the people around the sites that is recorded from the map data, which shows the places where the human have the most activity
The results using the CaffeNet and deep convolutional neural network are used for the process with an accuracy of around 90% and higher limit of about 96%, respectively
Summary
The following study presents the challenges in modern times the heritages face due to the encroached human activity and the environmental conditions. The use of machine learning has been proposed as the solution for the identification and recognition of the sites of cultural heritage and relics. China has been bestowed with numerous sites of heritage, which contains relics that are of great importance for the future to study the past. There are some significant challenges addressed in identifying the immovable culture relics. They are insufficient enforcement and development control, poor physical planning mechanism, and popular participation and poor funding. In order to predict cultural relics, many researchers have used support vector machine and radial basis function neural network method [2, 3]. The researcher found that the radial basis function neural network method has effective generalization and nonlinear mapping ability [4]. The paper presents the data from the identification process along with the detailed results of the machine algorithms used
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