Urban Tree Crown Detection based on Deep Learning and High-Resolution Aerial Imagery: PTCNet for Pullman, WA, USA

Alegbeleye, O. M.; Meddens, A. J. H.; Rotimi, Y. O.; Ibeh, K. G.

doi:10.1016/j.rsase.2025.101818

Back to Kelechi G. Ibeh Publications

Urban Tree Crown Detection based on Deep Learning and High-Resolution Aerial Imagery: PTCNet for Pullman, WA, USA

Authors: O. M. Alegbeleye, A. J. H. Meddens, Y. O. Rotimi, and K. G. Ibeh

Year: 2025

Journal: Remote Sensing Applications: Society and Environment

Article number: 101818

DOI: 10.1016/j.rsase.2025.101818

Research Areas

Remote sensing, deep learning, urban forestry, high-resolution aerial imagery, tree crown detection, tree canopy mapping, geospatial analysis, environmental monitoring, and urban vegetation assessment.

Plain-Language Summary

This study applies deep learning and high-resolution aerial imagery to detect individual urban tree crowns in Pullman, Washington, USA. The work contributes to urban forestry, tree canopy mapping, and scalable geospatial monitoring of urban vegetation.

Relevance

The paper is relevant to researchers and practitioners working on urban forest inventory, remote sensing, deep learning applications in environmental monitoring, and high-resolution vegetation mapping. It also supports broader work on geospatial approaches for assessing tree structure and ecosystem services in human-dominated landscapes.

Suggested Citation

Alegbeleye, O. M., Meddens, A. J. H., Rotimi, Y. O., & Ibeh, K. G. (2025). Urban Tree Crown Detection based on Deep Learning and High-Resolution Aerial Imagery: PTCNet for Pullman, WA, USA. Remote Sensing Applications: Society and Environment, 101818. https://doi.org/10.1016/j.rsase.2025.101818

Author Identity

This publication includes Kelechi G. Ibeh as coauthor. Related research interests include forest ecology, geospatial analysis, remote sensing, urban forestry, ecosystem services, and landscape-scale environmental monitoring.