  There is a rising interest in mapping trees using satellite or aerial
imagery, but there is no standardized evaluation protocol for comparing and
enhancing methods. In dense canopy areas, the high variability of tree sizes
and their spatial proximity makes it arduous to define the quality of the
predictions. Concurrently, object-centric approaches such as bounding box
detection usuallyperform poorly on small and dense objects. It thus remains
unclear what is the ideal framework for individual tree mapping, in regards to
detection and segmentation approaches, convolutional neural networks and
transformers. In this paper, we introduce an evaluation framework suited for
individual tree mapping in any physical environment, with annotation costs and
applicative goals in mind. We review and compare different approaches and deep
architectures, and introduce a new method that we experimentally prove to be a
good compromise between segmentation and detection.
