Optimizing Architectural Layouts and Spatial Configurations in Polar Environments Using Image Segmentation and Semantic Mapping Techniques

The extreme climatic and environmental conditions of polar regions impose stringent constraints on architectural safety, functionality, and adaptability.With the growing prevalence of scientific expeditions and resource exploration in these remote territories, the demand for resilient, efficient, and adaptive architectural solutions has increased substantially.However, conventional design methodologies have been found inadequate in addressing the multifaceted challenges posed by low temperatures, harsh illumination conditions, and limited spatial flexibility.In particular, standard image segmentation algorithms often underperform in polar indoor environments due to dynamic lighting variations, high reflectivity of ice and snow surfaces, and structural ambiguities.Additionally, existing optimization frameworks for architectural layouts frequently neglect the thermal inefficiencies induced by extreme cold, as well as the distinctive functional zoning requirements of polar buildings, such as isolation zones, decontamination chambers, and modular emergency units.To address these limitations, an integrated architectural optimization approach has been developed, combining image segmentation, semantic mapping, and spatial configuration modelling tailored for polar contexts.First, a planar image matching technique has been proposed, leveraging angular and distance-based features to extract object orientations and spatial relationships, thereby enhancing scene recognition robustness under variable visual conditions.Second, a semantic simultaneous localization and mapping (semantic SLAM) framework has been adapted for indoor architectural segmentation, enabling real-time integration of semantic information into the SLAM pipeline for high-precision spatial modelling and environmental interpretation.Third, a grid map-based optimization model has been constructed to quantify spatial attributes and incorporate environmental variables-such as thermal conductivity, wind flow, and material performance-into layout decision-making.Functional zoning constraints specific to polar operations have also been embedded within the optimization objective functions to ensure mission-specific spatial configurations.The innovations presented lie in the mitigation of polar-specific visual interference in image processing, the enhancement of architectural segmentation through semantic-augmented SLAM, and the development of an environmentally responsive spatial optimization framework.These contributions are expected to provide foundational support for intelligent, data-driven architectural design in extreme environments, while offering methodological advancements to the broader fields of remote architecture, robotics, and environmental informatics.