Smart Tourism Landmark Recognition: A Multi-Threshold Enhancement and Selective Ensemble Approach Using YOLO11

Ulugbek Hudayberdiev, Junyeong Lee () and Odil Fayzullaev
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Ulugbek Hudayberdiev: Department of Management Information Systems, Chungbuk National University, Cheongju 28644, Republic of Korea
Junyeong Lee: Department of Management Information Systems, Chungbuk National University, Cheongju 28644, Republic of Korea
Odil Fayzullaev: NextPath Innovations LLC., 10555 62nd Drive, Forest Hills, NY 11375, USA

Sustainability, 2025, vol. 17, issue 17, 1-26

Abstract: Automated landmark recognition represents a cornerstone technology for advancing smart tourism systems, cultural heritage documentation, and enhanced visitor experiences. Contemporary deep learning methodologies have substantially transformed the accuracy and computational efficiency of destination classification tasks. Addressing critical gaps in existing approaches, we introduce an enhanced Samarkand_v2 dataset encompassing twelve distinct historical landmark categories with comprehensive environmental variability. Our methodology incorporates a systematic multi-threshold pixel intensification strategy, applying graduated enhancement transformations at intensity levels of 100, 150, and 225 to accentuate diverse architectural characteristics spanning from fine-grained textural elements to prominent reflective components. Four independent YOLO11 architectures were trained using original imagery alongside systematically enhanced variants, with optimal epoch preservation based on validation performance criteria. A key innovation lies in our intelligent selective ensemble mechanism that conducts exhaustive evaluation of model combinations, identifying optimal configurations through data-driven selection rather than conventional uniform weighting schemes. Experimental validation demonstrates substantial performance gains over established baseline architectures and traditional ensemble approaches, achieving exceptional metrics: 99.24% accuracy, 99.36% precision, 99.40% recall, and 99.36% F1-score. Rigorous statistical analysis via paired t-tests validates the significance of enhancement strategies, particularly demonstrating effectiveness of lower-threshold transformations in capturing architectural nuances. The framework exhibits remarkable resilience across challenging conditions including illumination variations, structural occlusions, and inter-class architectural similarities. These achievements establish the methodology’s substantial potential for practical smart tourism deployment, automated heritage preservation initiatives, and real-time mobile landmark recognition systems, contributing significantly to the advancement of intelligent tourism technologies.

Keywords: smart tourism; YOLO11; tourist landmark recognition; cultural heritage preservation; image enhancement; ensemble deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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