Automated urban tree survey using remote sensing data, Google street view images, and plant species recognition apps

Urban tree inventories have mostly focused on the information of individual trees becausethese allows city authorities to efficiently plan urban forestation. However, single-tree urban tree inventories are expensive for municipalities, so the inventories lack detail and are often out of date. In this work, we aim to integrate the possibility of using online applications for automatic species identification with worldwide coverage Pl@ntNet and Plant.Id on Google Street View (GSV) images in order to perform cost-effective urban tree inventories at the single-tree level and evaluate the performance of the two applications through comparison with a locally trained neural network using an appropriate set of metrics. Our work showed that the Plant.Id application gave the best performance by correctly identifying plants in the city of Prato with a median accuracy of 0.73 and better performance for the most common plants: Pinus pinea 0.87, Tilia aeuropea 0.87, Platanus hybrida 0.89. The proposed method also has a limitation. Trees within parks, walking paths and private green areas cannot be photographed and identified because Google cars cannot access them. The solution to this limitation is to combine GSV images with spherical photos taken via light unmanned aircraft.