Augmented reality (AR) scene modeling with virtual-real integration is an effective way to enhance users’ perception and understanding of geographic spaces. However, the existing modeling methods focus on precise virtual-real alignment in static scenes using single-frame images, leading to inefficiencies in dynamic scene modeling and low accuracy in virtual-real integration. This paper proposes a fast modeling method for AR dynamic scenes with spatio-temporal semantic constraints. By thoroughly analyzing spatio-temporal semantic constraint rules in AR dynamic scene modeling, a keyframe extraction algorithm based on a synchronized spatio-temporal semantic distance measurement model was designed. A rapid spatio-temporal interpolation model for AR dynamic view poses with spatio-temporal semantic association was established, and a real 3D scene-driven fast twin modeling method for AR dynamic scenes was proposed. Experimental results show that the proposed method reduces redundant image matching computations by 87.53% while maintaining virtual-real registration accuracy above 1°. This method enables accurate sampling of keyframes with spatio-temporal homogeneity, avoids redundant transmission of large volumes of frame image data, and improves AR dynamic scene virtual-real registration efficiency while maintaining accuracy. Furthermore, the spatial semantic information in real 3D scenes effectively guides fast AR dynamic scene modeling.

Accurate and rapid localization can improve geographic information system (GIS) tasks to support disaster rescue and resource exploration in field operations. However, the existing localization methods suffer from high costs, low efficiency, and poor accuracy of long-distance targets due to environmental factors like terrain and landforms. Therefore, we propose a multilevel localization framework based on mobile phones for different field scenarios. First, we investigated a rapid localization method constrained by scene contextual features in the field with salient features. Second, we designed a single-point localization method that combines DEM data and mobile phones when high-precision DEM data are available for the region. Third, we studied a map-matching corrected joint localization with mobile phone images in the field lacking salient features and high-precision DEM data. Finally, we developed a prototype system for field localization and selected a forest scene for experimental analysis. The results showed that the proposed mobile phone-based localization framework supports long-distance localization in the field. The localization accuracy in three different field scenarios is within 100 meters, and the localization efficiency reaches a minute level, which can effectively support the convenient, rapid, high-precision, and long-distance localization tasks in field scenes.

The detail and representation of a spatial layout varies with scale. This affects an individual’s learning effectiveness and understanding, in turn directly influencing their behavior in a fire evacuation. However, the impact of layout learning methods with different spatial scales on fire evacuation behavior, and the relationship between spatial cognition and evacuation effects, remains unclear. We conducted spatial layout learning across three scales with 81 participants and simulated a fire evacuation scenario in a mobile virtual reality for groups. We collected evacuation decision-making and user experience questionnaires as supplementary data. The results demonstrate that small-scale learning objects are the easiest for participants to understand in terms of spatial layout and relationships, but their performance in fire evacuation is poor. Large-scale learning objects significantly improve participants’ evacuation efficiency. Spatial layout learning plays a crucial role in fire evacuation outcomes, but traditional spatial knowledge acquisition measurement methods cannot predict fire evacuation performance. This study sheds light on how spatial cognition influences fire evacuation behavior and provides a more reliable fire evacuation simulation method based on mobile virtual reality (MVR).