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Workflow for mapping and 3D-modeling of urban forests based on LiDAR point clouds

Authors: Münzinger, Markus;

Workflow for mapping and 3D-modeling of urban forests based on LiDAR point clouds

Abstract

This repository contains scripts for high-resolution area-wide mapping and 3D modeling of urban forests based on LiDAR point clouds. The workflow is designed for widely available LiDAR point clouds with a density of at least 4 pts/m². Our published research article: Münzinger, M., Prechtel, N., & Behnisch, M. (2022). Mapping the Urban Forest in Detail: From LiDAR Point Clouds to 3D Tree Models. Available at https://doi.org/10.1016/j.ufug.2022.127637. Processing Tasks: Task 1: Classification of the urban forest in an object-based data fusion approach combining the point cloud with multispectral aerial imagery and a 3D building model Task 2: Detection, segmentation and parameterization of individual tree crowns Task 3: Efficient reconstruction and 3D modeling of tree crowns using geometric primitives

Keywords

LiDAR point cloud classification, 3D City Modeling, Urban Forestry, R, Individual Tree Segmentation, CityGML

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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