Efficient and Accurate Tree Detection from 3D Point Clouds through Paid Crowdsourcing
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.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Super-Resolution of Airborne Laser Scanning Point Clouds for Forest Inventory
Airborne Laser Scanning (ALS) can collect point clouds across large areas, enabling large-scale forest inventory. However, ALS point clouds are sparse and noisy, resulting in inaccurate individual-tree-level forest inven…
Transfer LearningPoint CloudsBranchPoseNet: Characterizing tree branching with a deep learning-based pose estimation approach
This paper presents an automated pipeline for detecting tree whorls in proximally laser scanning data using a pose-estimation deep learning model. Accurate whorl detection provides valuable insights into tree growth patt…
Pose EstimationForest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds
Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more …
PointNLM: Point Nonlocal-Means for vegetation segmentation based on middle echo point clouds
Middle-echo, which covers one or a few corresponding points, is a specific type of 3D point cloud acquired by a multi-echo laser scanner. In this paper, we propose a novel approach for automatic segmentation of trees tha…
Point Cloud SegmentationSegmentationSemantic Segmentation3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions
In this paper, we propose a novel generative adversarial network (GAN) for 3D point clouds generation, which is called tree-GAN. To achieve state-of-the-art performance for multi-class 3D point cloud generation, a tree-s…
Generative Adversarial NetworkPoint Cloud Generation