Urban Forest Identification from High-Resolution Images Using Deep-Learning Method.

Urban forests can maintain urban ecological balance and improve environmental quality, but it is difficult to identify such forests accurately due to its complex and fragmented features. This study aims to develop a deep-learning network to extract the urban forest spatial distribution from high-spatial resolution image, like from Chinese Gaofen-2 (GF-2) image. Based on the GF-2 surface reflectance image and urban forest samples known in prior, this study firstly create a U-Net network to train and generate a predictive model to identify the urban forest, and then used the trained model to get the spatial distribution of urban forest in the Beibei district. Results showed that the U-Net Network can predict urban forests distribution accurately and rapidly.