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Journals / Türkiye Coğrafi Bilgi Sistemleri Dergisi / 2023 / Cilt: 5 Sayı: 1

Decoding Nature's Patterns: An Innovative Approach to Tree Detection Using Deep Learning and High-Resolution Aerial Imagery

Halil İbrahim ŞENOL
Abdurahman Yasin YİĞİT
Pages
52–59
Publication Date
2023-06-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results showcase an impressive accuracy rate of 80.87%, demonstrating the potential of deep learning in urban forestry applications and contributing to effective urban planning.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study investigates the application of deep learning algorithms and high-resolution aerial imagery for individual tree detection in urban areas, using a neighborhood in Mersin, Turkey, as a case study.
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Key Findings & Data:Employing the DeepForest Python package, we utilize high-resolution (7cm) aerial imagery to detect and map the city's tree population accurately.
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Conclusion & Impact:The results showcase an impressive accuracy rate of 80.87%, demonstrating the potential of deep learning in urban forestry applications and contributing to effective urban planning.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:GIS · Spatial Analysis · Aerial Imagery

Abstract

This study investigates the application of deep learning algorithms and high-resolution aerial imagery for individual tree detection in urban areas, using a neighborhood in Mersin, Turkey, as a case study. Employing the DeepForest Python package, we utilize high-resolution (7cm) aerial imagery to detect and map the city's tree population accurately. The results showcase an impressive accuracy rate of 80.87%, demonstrating the potential of deep learning in urban forestry applications and contributing to effective urban planning. The information generated from this study is crucial for conserving urban green spaces, enhancing resilience to climate change, and supporting urban biodiversity. While this research is focused on Mersin, the methods employed are globally adaptable, laying a foundation for further refinement and potential identification of different tree species in future work. This investigation highlights the transformative role of advanced technology in fostering sustainable urban environments.

Özet

This study investigates the application of deep learning algorithms and high-resolution aerial imagery for individual tree detection in urban areas, using a neighborhood in Mersin, Turkey, as a case study. Employing the DeepForest Python package, we utilize high-resolution (7cm) aerial imagery to detect and map the city's tree population accurately. The results showcase an impressive accuracy rate of 80.87%, demonstrating the potential of deep learning in urban forestry applications and contributing to effective urban planning. The information generated from this study is crucial for conserving urban green spaces, enhancing resilience to climate change, and supporting urban biodiversity. While this research is focused on Mersin, the methods employed are globally adaptable, laying a foundation for further refinement and potential identification of different tree species in future work. This investigation highlights the transformative role of advanced technology in fostering sustainable urban environments.

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Cite This Article

ŞENOL, H. İ., YİĞİT, A. Y. (2023). Decoding Nature's Patterns: An Innovative Approach to Tree Detection Using Deep Learning and High-Resolution Aerial Imagery. Türkiye Coğrafi Bilgi Sistemleri Dergisi, Cilt: 5 Sayı: 1, 52–59. https://ajindex.com/articles/947029

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