Indoor emergency response plays a critically important role in disaster management for cities, which must consider the evacuation of people in a dynamic indoor environment. The spatial model is the foundation for the specific analysis of indoor emergency responses, such as evacuations. The current spatial model for evacuation has three primary pitfalls: (1) it primarily focuses on static spatial information, such as rooms, doors, and windows, and lacks dynamic information, such as events and sensors; (2) it mainly focuses on the horizontal space and the static scene and lacks a multi-story component that considers the different properties of stairs compared to planar areas; and (3) it places emphasis on the indoor navigation calculation with a 2D/3D network, which lacks individual properties that can support more complicated analysis, such as congestion and stagnation. In this paper, we propose a dynamic indoor field model with three typical characteristics. (1) It includes not only static information but also dynamic information, such as outdoor and indoor building geometry, sensors, fire spread, and personnel behavior. (2) It supports multi-story buildings from the macro level (building level and floor level) to the micro level (room level and individual level) based on horizontal and vertical indoor space. (3) It supports spatial calculations based on a three-dimensional space grid and can analyze potential congestion and stagnation during evacuation. We design a corresponding evacuation method that supports individual evacuation route finding and evacuation assessment. We perform a series of analyses of the applicability of the proposed model and the efficiency of the designed evacuation method based on multi-story evacuation studies. The simulation includes a total evacuation population exceeding 7000 individuals, and the analysis suggests that the new model and algorithm are effective in planning indoor emergency routes that avoid potential congestion or stagnation.

The Open Geospatial Consortium (OGC) Geography Markup Language (GML) standard provides basic types and a framework for defining geo-informational data models such as CityGML and IndoorGML, which provide standard information models for 3D city modelling and lightweight indoor network navigation. Location information, which is the semantic engine that fuses big geo-information data, is however, discarded in these standards. The Chinese national standard of Indoor Multi-Dimensional Location GML (IndoorLocationGML) presented in this study can be used in ubiquitous indoor location intelligent applications for people and robots. IndoorLocationGML is intended as an indoor multi-dimensional location information model and exchange data format standard, mainly for indoor positioning and navigation. This paper introduces the standard’s main features: (1) terminology; (2) indoor location information model using a Unified Modeling Language (UML) class diagram; (3) indoor location information markup language based on GML; and (4) use cases. A typical application of the standard is then discussed. This standard is applicable to the expression, storage, and distribution of indoor multi-dimensional location information, and to the seamless integration of indoor–outdoor location information. The reference and basis are therefore relevant to publishers, managers, users, and developers of indoor navigation and location-based services (LBS).

Keywords: Indoor, Location Model, Multi-dimensional, Indoor Navigation Aiming at the increasing requirements of seamless indoor and outdoor navigation and location service, a Chinese standard of Multidimensional Indoor Location Information Model is being developed, which defines ontology of indoor location. The model is complementary to 3D concepts like CityGML and IndoorGML. The goal of the model is to provide an exchange GML-based format for location needed for indoor routing and navigation. An elaborated user requirements analysis and investigation of state-of-the-art technology in expressing indoor location at home and abroad was completed to identify the manner humans specify location. The ultimate goal is to provide an ontology that will allow absolute and relative specification of location such as "in room 321", "on the second floor", as well as, "two meters from the second window", "12 steps from the door".

Most existing image aesthetic assessment methods rely on global representations or simple aggregation of local features. This makes it difficult to model semantic region information and provide region-level explanations. To address this issue, we propose a Semantic Segmentation-Guided Cross-Attention Fusion (SSG-CAF) method for explainable image aesthetic assessment. The method first uses SegFormer-B0 to generate semantic masks for eight semantic categories, thereby introducing explicit semantic and spatial priors. It then combines region-level features with global visual context. A cross-attention mechanism is used to model dependencies between semantic regions and the global representation. This design supports the representation of high-level aesthetic cues, including compositional balance, subject prominence and regional harmony. SHAP is further used for region-level attribution analysis. Experiments on the cleaned AVA test set showed that SSG-CAF achieved an MSE of 0.3412 ± 0.0150, an MAE of 0.4565 ± 0.0103, a PLCC of 0.6517 ± 0.0029 and an SRCC of 0.6370 ± 0.0034. Compared with the selected baselines, SSG-CAF showed stronger correlations with human aesthetic ratings under the cleaned AVA protocol. It also produced post-hoc, quantitative region-level attribution evidence that describes model behaviour.