Context-aware landmark recognition in location-based augmented reality: A switching framework with a MobileNet backbone for cultural heritage engagement

City landmarks play a pivotal role in fostering a deeper understanding of urban environments and in supporting navigation, wayfinding, and cultural education and engagement. One of the primary objectives of sustainable and smart city management is the design of pervasive computing infrastructures that enable the discovery and retrieval of spatial and non-spatial information from landmarks and points of interest (POIs) in any given context. A promising solution lies in location-based augmented reality (LBAR) platforms for smartphones. Previous research has typically adopted two separate approaches: first, location and inertia sensors-based LBAR systems, which face limitations such as GPS errors, narrow fields of view, and sensor malfunctions; and second, image-based deep learning (DL) systems, typically demand large datasets, impose heavy computational loads, and may introduce latency in real-time applications. To address these gaps, we propose a switching-based pervasive AR design that combines LBAR, DL, and context-awareness. This framework is a complementary switching mechanism in which DL serves as a fallback when LBAR fails to identify a landmark. For this purpose, convolutional neural network (CNN) methods and their lightweight pre-trained variants, such as MobileNetV2, were employed. In addition, we propose a high level of context-awareness for cultural heritage engagement, by incorporating parameters such as time of day, user age, ongoing events, and spatial distance. Importantly, our study adopts an end-to-end approach encompassing dataset creation, web-service development, mobile application implementation, and real-world field testing. Experiments conducted on ten well-known landmarks in Tehran, using images curated from social media and the Internet, confirm the effectiveness of the integrated system. MobileNetV2 achieved reliable real-time landmark detection with 0.9100 ± 0.0215 accuracy and 0.9010 ± 0.0249 macro-F1. The switching LBAR–DL framework improved detection accuracy by approximately 34% compared with LBAR alone, yielding meaningful statistical results for the ablation analysis. User-centered evaluations with volunteers indicated that 90% of respondents were satisfied with overcoming LBAR limitations, and 68% reported a positive experience in context-aware understanding from landmarks. Finally, a GIS-assisted sustainable development analysis in Tehran demonstrated the potential of the proposed system in smart and sustainable city planning.