Urban forests play an important role in urban ecosystems. They can not only beautify the urban environment but also help protect biodiversity and maintain ecological balance. Effective urban forest management is a basic requirement to ensure sustainable development. Traditional urban forest management usually requires the investment of a lot of materials and labor to conduct field research. RGB high-resolution aerial images have emerged as an efficient source of data for use in the detection and mapping of individual trees in urban areas. In recent years, there has been impressive progress in the field of deep learning methods for use in object detection. Semi-supervised learning is an effective way to deal with the problem that deep learning requires a large amount of labeled data. In this paper, we proposed an improved faster region-based convolutional neural network (Faster R-CNN) with Swin transformer method. Based on existing datasets, the model was trained and then transferred to new datasets. The method was evaluated within three distinct urban areas: a green space, a residential area and a suburban area. The experimental results indicate that our method achieved higher performance than other Faster R-CNN models. This method provides a reference in automated individual tree detection based on high-resolution images in urban areas for urban forestry managers.

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