Spectral Semantic Analytics of Local Spaces via Spectral-Semantic Neural Network Metamodel

Abstract

This paper investigates the development and deployment of spectral-semantic analytical frameworks for local space analysis using neural networks. The proposed paradigm unifies spectral data processing—with measurements collected from spectrometers and thermal imagers—and semantic modeling, which frames spectral features as bearers of structured semantic information. This work elaborates on techniques for translating spectral modality into linguistic-semantic representations; this transformation enables dual characterization of local physical properties: quantitative description via spectral parameters, and qualitative description via semantic profiles and patterns. Special focus is given to building spectral-semantic dictionaries and matching neural architectures that extract and formalize contextual correlations between object spectral signatures and their semantic implications. Diverse spectrogram formats and spectral representations (multiband and hyperspectral data included) are adopted for local space characterization, alongside a neural network metamodel capable of generating domain-specific spectral-semantic models. Attention modules are embedded into these architectures to automatically screen high-value spectral bands and spatial regions pivotal to semantic reasoning.