Learned and Native Concepts in Latent Representations of Terrain Images

Pylyp Prystavka; Serge Dolgikh; Olha Cholyshkina; Oleksandr Kozachuk

doi:10.1007/978-3-031-20834-8_4

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Learned and Native Concepts in Latent Representations of Terrain Images

Pylyp Prystavka Serge Dolgikh Olha Cholyshkina Oleksandr Kozachuk

Year: 2022 Communications in computer and information science Pages: 64-84 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-031-20834-8_4

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Keywords:

Autoencoder Generative grammar Computer science Artificial intelligence Terrain Convolutional neural network Generative model Field (mathematics) Unsupervised learning Process (computing) Machine learning Deep learning Pattern recognition (psychology) Geography Mathematics Cartography

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Image Processing and 3D Reconstruction

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Advanced Image and Video Retrieval Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Image Retrieval and Classification Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Learned and Native Concepts in Latent Representations of Terrain Images

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