Efficient and Accurate Tree Detection from 3D Point Clouds through Paid Crowdsourcing

TL;DR

This paper introduces a rapid, crowdsourcing-based method for accurate tree detection in 3D point clouds, utilizing a web tool to enable non-experts to produce high-quality annotations efficiently.

Contribution

The paper presents a novel web tool and methodology for crowdsourcing tree annotations in 3D point clouds, achieving over 90% quality in diverse test sets.

Findings

Quality of annotations exceeds 90%

Annotation process completed within 1-2 days

Effective handling of noise in crowdsourced data

Abstract

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.