Using machine learning to identify urban forest crown bounding boxes (CBB): Exploring a new method to develop urban forest policy

Collecting and managing individual tree data is a critical activity for green sustainability strategies. Local governments are able to easily collect detailed public tree inventories, however data on trees located on private land are much more challenging and costly to collect. This means that new regulations to limit the removal of trees on private land go untested prior to their implementation, or fail to pass regulatory review processes. Without knowledge of the location of trees or the range of their different sizes, Local Government Authorities (LGAs) are unable to predict where a new policy to prohibit the removal of trees of a certain size is likely to have the greatest effect, where enforcement should be concentrated, or to convince government, the development sector and local communities of the need for action to preserve trees. The aim of this study was to explore the potential of a supervised machine learning algorithm as a cost-efficient method to understand tree sizes and locations on private land and to discuss how this information could be used for sustainable urban greening. We conclude by discussing some of the affordances of this approach to better target native vegetation protection and protect large trees; and report on the precision and recall of the detection of the urban forest.