Usage of Artificial Intelligence in Smart Tourism: A CNN-based Landmark Classification for Diyarbakir Province

Yunus Korkmaz

Makale detayları

Usage of Artificial Intelligence in Smart Tourism: A CNN-based Landmark Classification for Diyarbakir Province

Yazarlar:

Yunus Korkmaz

Yayıncı:

Fırat University

Ana Başlıklar:

Derin Öğrenme | Yapay Zeka (Diğer)

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.37 Issue 2, pp.723-735

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001357260

pISSN:

1308-9072

Anahtar kelimeler:

Özet:

 The integration of artificial intelligence AI in the tourism sector has emerged as a transformative approach to enhance the travel experiences and promote regional attractions. This study explores the application of AI in smart tourism by developing a convolutional neural network CNN-based landmark classification system for Diyarbakır Province, a culturally rich region in South-East Turkey. The method specifically focuses on classifying images of five popular landmarks in Diyarbakır, leveraging state-of-the-art AI techniques to enhance regional visibility and tourist engagement. To achieve this, a pre-trained CNN model, namely AlexNet, was fine-tuned for the landmark classification task. By adapting the parameters of AlexNet to the specific dataset, the model was optimized for improved feature extraction and classification accuracy for totally 5 classes places. The proposed framework was evaluated using a carefully curated dataset, yielding a remarkable 97.5 classification accuracy on the test set. The performance of the proposed model highlights the reliability and effectiveness of the methodology in accurately identifying landmarks, even with complex architectural and environmental features. These results can concretely contribute to both academic research and practical applications by providing a lightweight and accurate model that can be embedded into mobile travel applications or digital tourism platforms for real-time landmark recognition, enhancing tourist engagement and regional visibility.

 The integration of artificial intelligence AI in the tourism sector has emerged as a transformative approach to enhance the travel experiences and promote regional attractions. This study explores the application of AI in smart tourism by developing a convolutional neural network CNN-based landmark classification system for Diyarbakır Province, a culturally rich region in South-East Turkey. The method specifically focuses on classifying images of five popular landmarks in Diyarbakır, leveraging state-of-the-art AI techniques to enhance regional visibility and tourist engagement. To achieve this, a pre-trained CNN model, namely AlexNet, was fine-tuned for the landmark classification task. By adapting the parameters of AlexNet to the specific dataset, the model was optimized for improved feature extraction and classification accuracy for totally 5 classes places. The proposed framework was evaluated using a carefully curated dataset, yielding a remarkable 97.5 classification accuracy on the test set. The performance of the proposed model highlights the reliability and effectiveness of the methodology in accurately identifying landmarks, even with complex architectural and environmental features. These results can concretely contribute to both academic research and practical applications by providing a lightweight and accurate model that can be embedded into mobile travel applications or digital tourism platforms for real-time landmark recognition, enhancing tourist engagement and regional visibility.

 The integration of artificial intelligence AI in the tourism sector has emerged as a transformative approach to enhance the travel experiences and promote regional attractions. This study explores the application of AI in smart tourism by developing a convolutional neural network CNN-based landmark classification system for Diyarbakır Province, a culturally rich region in South-East Turkey. The method specifically focuses on classifying images of five popular landmarks in Diyarbakır, leveraging state-of-the-art AI techniques to enhance regional visibility and tourist engagement. To achieve this, a pre-trained CNN model, namely AlexNet, was fine-tuned for the landmark classification task. By adapting the parameters of AlexNet to the specific dataset, the model was optimized for improved feature extraction and classification accuracy for totally 5 classes places. The proposed framework was evaluated using a carefully curated dataset, yielding a remarkable 97.5 classification accuracy on the test set. The performance of the proposed model highlights the reliability and effectiveness of the methodology in accurately identifying landmarks, even with complex architectural and environmental features. These results can concretely contribute to both academic research and practical applications by providing a lightweight and accurate model that can be embedded into mobile travel applications or digital tourism platforms for real-time landmark recognition, enhancing tourist engagement and regional visibility.

 Yapay zekanın turizm sektörüne entegrasyonu, seyahat deneyimlerini geliştirmek ve bölgesel cazibe merkezlerini tanıtmak için dönüştürücü bir yaklaşım olarak ortaya çıkmıştır. Bu çalışma, Türkiyenin Güneydoğusunda yer alan kültürel açıdan zengin bir bölge olan Diyarbakır ili için Evrişimli Sinir Ağı CNN tabanlı bir turistik yer noktası sınıflandırma sistemi geliştirerek akıllı turizmde yapay zeka uygulamasını araştırmaktadır. Yöntem, özellikle bölgesel görünürlüğü ve turist katılımını artırmak için en son yapay zeka tekniklerinden yararlanarak, Diyarbakırdaki beş popüler turistik yer görüntülerini sınıflandırmaya odaklanmaktadır. Bunu başarmak için, AlexNet adlı önceden eğitilmiş bir CNN modeli, turistik yer sınıflandırma görevi için ince ayarlanmıştır. AlexNetin parametrelerini belirli veri setine uyarlayarak, model toplam 5 sınıf yer için iyileştirilmiş özellik çıkarma ve sınıflandırma doğruluğu için optimize edilmiştir. Önerilen model, dikkatlice düzenlenmiş bir veri seti kullanılarak değerlendirilmiş ve test setinde 97,5lik dikkate değer bir sınıflandırma doğruluğu elde edilmiştir. Önerilen modelin performansı, karmaşık mimari ve çevresel özelliklere sahip olsa bile turistik yer noktalarını doğru bir şekilde tanımlamada metodolojinin güvenilirliğini ve etkinliğini vurgulamaktadır. Bu çalışmanın sonuçları, mobil seyahat uygulamalarına entegre edilebilecek hafif ve doğru bir model sunarak, hem akademik çalışmalara hem de turistik etkileşimi arttırabilecek pratik uygulamalara katkı sağlamaktadır.

Makale detayları

Usage of Artificial Intelligence in Smart Tourism: A CNN-based Landmark Classification for Diyarbakir Province

Yazarlar:

Yunus Korkmaz

Yayıncı:

Fırat University

Ana Başlıklar:

Derin Öğrenme | Yapay Zeka (Diğer)

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.37 Issue 2, pp.723-735

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001357260

pISSN:

1308-9072

Smart tourism

artificial intelligence

deep learning

landmark classificationAkıllı turizm

yapay zeka

derin öğrenme

turistik yer sınıflandırma

Sayı

Makale detayları

Yazarlar: Yunus Korkmaz

Yayıncı: Fırat University

Ana Başlıklar: Derin Öğrenme | Yapay Zeka (Diğer)

Dergi: Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.37 Issue 2, pp.723-735

Belge Tipi: Makale

Yayın Tarihi: 9/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001357260

pISSN: 1308-9072

Sayı

Yazarlar:

Yunus Korkmaz

Yayıncı:

Fırat University

Ana Başlıklar:

Derin Öğrenme | Yapay Zeka (Diğer)

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.37 Issue 2, pp.723-735

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001357260

pISSN:

1308-9072