TY  - JOUR
T1  - Decoding Nature's Patterns: An Innovative Approach to Tree Detection Using Deep Learning and High-Resolution Aerial Imagery
TT  - Decoding Nature's Patterns: An Innovative Approach to Tree Detection Using Deep Learning and High-Resolution Aerial Imagery
AU  - Şenol, Halil İbrahim
AU  - Yiğit, Abdurahman Yasin
PY  - 2023
DA  - June
DO  - 10.56130/tucbis.1307926
JF  - Türkiye Coğrafi Bilgi Sistemleri Dergisi
JO  - TUCBİS
PB  - Lütfiye KUŞAK
WT  - DergiPark
SN  - 2687-5179
SP  - 52
EP  - 59
VL  - 5
IS  - 1
LA  - en
AB  - 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.
KW  - GIS
KW  - Spatial Analysis
KW  - Aerial Imagery
KW  - Deep Learning
KW  - Photogrammetry
N2  - 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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UR  - https://doi.org/10.56130/tucbis.1307926
L1  - https://dergipark.org.tr/en/download/article-file/3180507
ER  -