  Accurately quantifying tree cover is an important metric for ecosystem
monitoring and for assessing progress in restored sites. Recent works have
shown that deep learning-based segmentation algorithms are capable of
accurately mapping trees at country and continental scales using
high-resolution aerial and satellite imagery. Mapping at high (ideally
sub-meter) resolution is necessary to identify individual trees, however there
are few open-access datasets containing instance level annotations and those
that exist are small or not geographically diverse. We present a novel
open-access dataset for individual tree crown delineation (TCD) in
high-resolution aerial imagery sourced from OpenAerialMap (OAM). Our dataset,
OAM-TCD, comprises 5072 2048x2048 px images at 10 cm/px resolution with
associated human-labeled instance masks for over 280k individual and 56k groups
of trees. By sampling imagery from around the world, we are able to better
capture the diversity and morphology of trees in different terrestrial biomes
and in both urban and natural environments. Using our dataset, we train
reference instance and semantic segmentation models that compare favorably to
existing state-of-the-art models. We assess performance through k-fold
cross-validation and comparison with existing datasets; additionally we
demonstrate compelling results on independent aerial imagery captured over
Switzerland and compare to municipal tree inventories and LIDAR-derived canopy
maps in the city of Zurich. Our dataset, models and training/benchmark code are
publicly released under permissive open-source licenses: Creative Commons
(majority CC BY 4.0), and Apache 2.0 respectively.
