Estimating the parameters of trypillia proto-cities using plans and magnetic surveys with the help of neural networks.
Authors:   Donbas State Engineering Academy, Ukraine, o Melnykov , A Kapeleshchuk ,   Donbas State Engineering Academy, Ukraine
Published: October 2024

The Tryplian culture was considered the largest of all other archaeological cultures that existed on the territory of modern Ukraine. The method for analyzing magnetic images has been described, which allows archaeologists to assess the scale of settlements without excavating them. It is noted that one of the tasks during the analysis of the settlement is to find out its characteristics: counting the number of buildings, calculating the area, etc. However, in the majority of cases, counting the number of structures is currently unfeasible, as Trypillian proto-cities are situated within the cultural layer relatively close to the surface, and any economic activities disturb this cultural layer. The capabilities of the existing system (application) are described, which solved the problem by the method of average values and had differences from the commonly accepted method. It was concluded that a more automated version of this system could be an option where the number of sites in the image will be calculated by the average number of pixels per site, that is, the number of black and gray pixels in the image, divided by the average number of pixels in the site. It was decided to use neural network models. As an example, the largest of the famous Trypil proto-cities in Ukraine - Talyanka with an area of 450 hectares - is considered. Pictures taken between 1971 and 1974 were used because they are in the public domain. A list of actions for image preprocessing is described. A decision was made to train the model to input square images of 15 by 15 pixels, for this the entire image was divided into 748 square images, the number of buildings in each of them was determined manually. A four-point work algorithm is formulated, and it is also presented in the form of a UML class diagram and an activity diagram. The algorithm for creating a training sample for the second neural network from four points is also formulated (for the case when the zone of squares with lost information will run along the entire length of the picture, as it happens in the photo of Talyanok), presented in the form of a UML activity diagram. The neural network will accept 10 values as input, and will output one - the number of sites in the square, information about which is lost. Tensorflow and keras frameworks were used to create all models. The most successful model has almost 2.5 million parameters, the model requires 9.36 MB of RAM. During the tests, it was found that increasing the number of convolution layers does not increase the result. For testing the first model, a picture of the settlement of Maidanetske was submitted, for training a picture of the settlement of Talyanka was used. For the training set, the recognition accuracy was 92%, for testing - 86.5%. The neural network, which implements the algorithm for predicting the number of buildings on lost plots, provides an accuracy of 58% for the Talyanka settlement and 42% for the Maidanetske settlement. This accuracy is much better than a random guess, the probability of which is just over 6%.