Assessment of CNN-Based Methods for Single Tree Detection on High-Resolution RGB Images in Urban Areas

Maintaining vegetation cover in cities is a key component to keep cities safe and resilient. The monitoring of trees is usually done with LiDAR data or multi and hyperspectral images. In this sense, remote sensing RGB images are presented as a cheaper and easier processing solution. Here, we proposed to evaluate deep learning-based methods combined with high-resolution RGB images to detect single-trees in the urban environment. Three state-of-the-art methods are tested: Faster-RCNN, RetinaNet, and ATSS. A total of 220 images were used, in which we manually labeled 3382 trees. For the proposal task, our findings show that ATSS is 3% more accurate than Faster-RCNN and 4% than RetinaNet. However, in a qualitative inspection, Faster-RCNN and RetinaNet seems to be better at this task. Our findings shows the need of further research for developing suitable tools for urban tree detection. This tools can help cities top achieve a more sustainable and resilient environment especially to face climate change.