Scopus
YÖKSİS DOI Eşleşti
SJR Q2
Integrating advanced remote sensing technologies and machine learning in urban forestry: a comprehensive review and future outlook
Measurement Science and Technology · Haziran 2025
Özet
Urban forestry is of pivotal significance in the context of fostering sustainable and resilient cities. However, conventional monitoring and management methodologies are characterized by their labor-intensiveness and inefficiency. Recent advancements in machine learning (ML) offer transformative opportunities to enhance the automation, scalability, and accuracy of urban forest analysis. This critical review discusses the integration of ML with advanced remote sensing technologies—including satellite imagery, LiDAR, photogrammetry, and mobile mapping—to revolutionize urban forestry practices. In comparison to previous studies that primarily focus on isolated applications of ML, this review provides a comprehensive synthesis of state-of-the-art methodologies, bridging the gap between ML-driven automation and practical urban forestry management. Key topics include vegetation classification, point cloud data extraction, disease detection and species distribution mapping. Beyond these fundamental tasks, the study highlights pioneering applications such as the creation of digital twins of urban forests, which enable real-time monitoring and predictive modeling of tree health, distribution, and ecosystem services. By critically evaluating existing methodologies, their effectiveness, and emerging trends, this paper identifies the most promising ML strategies for optimizing urban forestry management. Furthermore, this review outlines current challenges, such as data availability, algorithmic biases, and computational constraints, while proposing future research directions to enhance the integration of ML in urban green space planning. This study presents a structured assessment of ML applications in urban forestry and serves as a valuable reference for researchers, policy makers and urban planners. The assessments promote the effective use of ML to enhance the ecological, social and economic functions of urban forests, supporting the long-term health and sustainability of these essential ecosystems.
YÖKSİS Kayıtları
Integrating advanced remote sensing technologies and machine learning in urban forestry: a comprehensive review and future outlook
Measurement Science and Technology · 2025 SCI-Expanded
Doç. Dr. MUSTAFA ZEYBEK →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Bu dergide (ISSN eşleşmesi) kurumun 2 kaydı bulundu.
YÖKSİS Kayıtları — ISSN Eşleşmesi
Bu dergide (ISSN eşleşmesi) kurumun 2 kaydı bulundu.
Road surface and inventory extraction from mobile LiDAR point cloud using iterative piecewise linear model
2023 ISSN: 0957-0233 SCI-Expanded Q3
Doç. Dr. MUSTAFA ZEYBEK →
Integrating advanced remote sensing technologies and machine learning in urban forestry: a comprehensive review and future outlook
2025 ISSN: 0957-0233 SCI-Expanded Q1
Doç. Dr. MUSTAFA ZEYBEK →
Makale Bilgileri
Toplam Atıf
5 atıf
· Scopus
ISSN09570233
Yayın TarihiHaziran 2025
Cilt / Sayfa36
Scopus ID2-s2.0-105008436835
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Havuzumuzdaki Atıflar 0
Bu makaleye, sistemimizdeki Scopus veritabanında bulunan 0 makale atıf yapmıştır. Scopus genel atıf sayısı: 5.
Bu makaleye, kendi Scopus havuzumuzdaki başka bir makaleden atıf kaydı bulunmuyor.
Scimago Dergi (ISSN Eşleşmesi)
Measurement Science and Technology
Q2
SJR Skoru0,582
H-Index165
YayıncıIOP Publishing Ltd.
ÜlkeUnited Kingdom
Applied Mathematics (Q2)
Engineering (miscellaneous) (Q2)
Instrumentation (Q2)
Metrikler
5
Atıf