Applying attention u-net with pytorch architectural add-ons for extensive hyperparameter search with weights & biases for area of visibility prediction based on terrain
https://doi.org/10.21122/2309-4923-2025-2-
Abstract
Current level of development in the sphere of deep learning allows replacing existing domain-specific algorithms for military simulation with approximating neural networks. Hyperparameter search allows finding network’s architecture, appropriate for a task. This work describes that process for the task of pre- dicting area of optical visibility, taking a fragment of a digital map as input and proposes ancillary architectural solutions for stitching building blocks together, assuring their conformation for performing search among their pos- sible combinations within the architectural space. The final proposed result is a channel-wise attention U-Net with an encoder, based on ResNet50 backbone.
About the Author
E. V. Rulko
Military Academy of the Republic of Belarus
Belarus
Belarus
Eugene Rulko, РhD, associate professor in computer science. The head of the research laboratory of military operation simulation of the educational institution «Military academy of the Republic of Belarus».
Minsk, Republic of Belarus
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Review
For citations:
Rulko E.V. Applying attention u-net with pytorch architectural add-ons for extensive hyperparameter search with weights & biases for area of visibility prediction based on terrain. «System analysis and applied information science». 2025;(2):4-10. https://doi.org/10.21122/2309-4923-2025-2-
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