  Accurate tree detection is of growing importance in applications such as
urban planning, forest inventory, and environmental monitoring. In this
article, we present an approach to creating tree maps by annotating them in 3D
point clouds. Point cloud representations allow the precise identification of
tree positions, particularly stem locations, and their heights. Our method
leverages human computational power through paid crowdsourcing, employing a web
tool designed to enable even non-experts to effectively tackle the task. The
primary focus of this paper is to discuss the web tool's development and
strategies to ensure high-quality tree annotations despite encountering noise
in the crowdsourced data. Following our methodology, we achieve quality
measures surpassing 90% for various challenging test sets of diverse
complexities. We emphasize that our tree map creation process, including
initial point cloud collection, can be completed within 1-2 days.
