Mapping of urban forest canopy height through diffusion-enhanced deep learning by using sentinel-2 and urban geospatial data
Year: 2025
Type: article
Abstract: Urban canopy height is a key indicator for assessing urban ecosystem services and ecological functions. However, the fragmentation and heterogeneity of urban forests pose challenges to the deep learning methods that perform well in large-scale forest canopy height mapping. To address this, we propose a novel and scalable Multimodal Urban Forest Canopy Height (MUFCH) estimation model that integrates diffusion mechanism and geospatial auxiliary data. Experimental results show that the MUFCH estimation model achieves a mean absolute error (MAE) of 2.019 m and a root mean square error (RMSE) of 3.090 m, representing improvements of 26.39% and 19.28%, respectively, compared to the baseline model. This enables high-precision canopy mapping with open-access data, providing a cost-effective solution for assessing critical forest indicators.
Authors: Nuo Xu, Yanqi Dong, Jiali Zi, Yi Xia, Fu Xu, Chao Mou, Feixiang Chen
Institution: Beijing Forestry University
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,390
Awards: G3482437501, G4538521706, G940072281
OpenAlex ID: W4414478319