title: ÐÐ¾ÑÑÑÐ¾ÐµÐ½Ð¸Ðµ Ð»Ð°Ð½Ð´ÑÐ°ÑÑÐ½ÑÑ ÐºÐ°ÑÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Construction of landscape maps based on generative neural networks
Creators: name: ÐÐ³Ð½Ð°ÑÑÐµÐ², ÐÐ°Ð½Ð¸Ð¸Ð»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: ÐºÐ°ÑÑÑ Ð²ÑÑÐ¾Ñ
Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐµ ÑÐµÑÐ¸
ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐ¸Ð½ÑÐµÐ· Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ñ Ð»Ð°Ð½Ð´ÑÐ°ÑÑÐ°
height map
gan
cnn
image syntesis
terrain generation

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description: The topic of the final qualification work: "Construction of landscape maps based on generative neural networks". This work is devoted to the study of the applicability of neural networks in the field of generating landscapes like the earth. The scope of application is the synthesis of landscapes close to realistic. Research methods - analysis, experiment, testing, comparison. Tasks that were solved during the study: 1. Search for initial data and generation of a training set. 2. Development of two different architectures of neural networks. 3. Training of implemented algorithms on various data sets. 4. Evaluation and visualization of learning outcomes of neural networks. The work was based on articles from the Internet. Programming and analysis was carried out using the interactive cloud environment Google Colab. The task and training of neural networks was carried out in Python using the open library for machine learning Keras. The program that prepares the training dataset is implemented in the C++ programming language using the stb_image open library for working with images. Visualization of the results was carried out in the cross-platform environment for the development of computer games Unity. As a result of the study, two neural networks were programmed and trained, their advantages and disadvantages were identified, and the results of generating neural networks were obtained and visualized in three-dimensional models.