Automatic Tree Annotation in LiDAR Data

Ananya Gupta

1,2

, Jonathan Byrne

, David Moloney

, Simon Watson

and Hujun Yin

School of Electrical and Electronic Engineering, The University of Manchester, Manchester, U.K.

Movidius Group, Intel Corporation, Dublin, Ireland

Keywords:

Airborne LiDAR, Urban Areas, Classiﬁcation, Tree Detection, Voxelization.

Abstract:

LiDAR provides highly accurate 3D point cloud data for a number of tasks such as forest surveying and urban

planning. Automatic classiﬁcation of this data, however, is challenging since the dataset can be extremely large

and manual annotation is labour intensive if not impossible. We provide a method of automatically annotating

airborne LiDAR data for individual trees or tree regions by ﬁltering out the ground measurements and then

using the number of returns embedded in the dataset. The method is validated on a manually annotated dataset

for Dublin city with promising results.

1 INTRODUCTION

Trees are critical to the healthy functioning of the

ecosystem and provide a number of beneﬁts to the en-

vironment such as regulation of water systems, main-

taining air quality, carbon sequestration and promot-

ing biodiversity. Hence, up-to-date tree inventories

are extremely important for monitoring and preserva-

tion of ecological environments so much so, that, one

of the key items on the top ten initiatives of the World

Economic Forum on the Future of Cities in 2015 was

to increase green canopy (Treepedia, 2015).

LiDAR sensors are a good tool for acquiring dense

point cloud data for the purpose of surveying in short

ranges. These sensors measure the distance by tim-

ing a laser pulse reﬂected from a target and have been

applied in a number of remote sensing applications

ranging from mapping (Schwarz, 2010), landslide in-

vestigations (Jaboyedoff et al., 2012) to tree inven-

tories (Shendryk et al., 2016b). LiDAR systems are

particularly suited to surveying forest canopies due

to their active sensors and their ability to penetrate

canopies.

Currently, most of the research on isolating trees

focuses on forested areas, with little emphasis given

to urban areas where there are more complex environ-

ments due to the presence of multiple types of natural

and artiﬁcial objects. However, surveying trees in ur-

ban areas is of paramount importance for applications

such as city planning, estimating green canopy of ar-

eas and monitoring solar radiations (Jochem et al.,

2009).

In this work, we provide an algorithm to identify

trees in urban areas using information embedded in

the LiDAR data without requiring human interven-

tion. Contrary to the common use of the Canopy

Height Model (CHM) for this task, our method works

directly with the LIDAR data and uses the number of

returns information from the LiDAR data to isolate

trees.

2 RELATED WORK

A number of methods have been developed to seg-

ment trees in LiDAR data with the most common be-

ing based on CHM (Lu et al., 2014; Ferraz et al.,

2016; Reitberger et al., 2009; Smits et al., 2012;

Mongus and Zalik, 2015).

Hyyppa et al. (2001) pioneered work in this area;

they used the information from the highest laser re-

turns to build a tree height model and then used region

growing techniques to for tree segmentation. Koch

et al. (2006) used local maximum ﬁlters to identify

potential tree regions followed by the use of a pour-

ing algorithm and knowledge-based assumptions to

identify tree crowns. Li et al. (2012) took advan-

tage of the spacing between treetops at their highest

points to identify trees and used a region growing al-

gorithm to segment them. More recently, Shendryk

et al. (2016a) used Euclidean distance clustering to

delineate trunks in eucalypt forests. These methods

proved highly effective in identifying trees in forested

areas but are unsuitable for use in urban environments

Gupta, A., Byrne, J., Moloney, D., Watson, S. and Yin, H.

Automatic Tree Annotation in LiDAR Data.

DOI: 10.5220/0006668000360041

In Proceedings of the 4th International Conference on Geographical Information Systems Theor y, Applications and Management (GISTAM 2018), pages 36-41

ISBN: 978-989-758-294-3

Figure 1: LiDAR point cloud showing decreasing number

of returns where Red>Yellow>Green>Blue.

since the assumption of highly dense collections of

trees does not apply to isolated individual trees.

Pioneering work in urban tree detection was based

on machine learning techniques. Secord and Zakhor

(2007) used a combination of aerial images and Li-

DAR data for segmentation and classiﬁcation with

Support Vector Machines (SVM). They extended this

work to using features derived from depth images of

LiDAR data with an SVM classiﬁer (Chen and Za-

khor, 2009). Carlberg et al. (2009) used a cascade

of binary classiﬁers to progressively identify water,

ground, roofs and trees by conducting 3D shape anal-

ysis. Segmenting foreground and background and

classifying object-like clusters was used to locate dif-

ferent 3D objects in an urban environment (Golovin-

skiy et al., 2009). Decision trees and Artiﬁcial Neural

Networks have also been used for segmenting features

from Digital Surface Models for classiﬁcation (H

oﬂe

et al., 2012). The main drawback with these meth-

ods is that they need pre-labelled data in order to

train their models and cannot work in an unsupervised

manner.

There has been some work done on identifying

trees in urban environments without the need for la-

belled data. Liu et al. (2013) proposed a method for

extracting tree crowns by ﬁltering out ground points

and using a spoke wheel method to get tree edges. Wu

et al. (2013) proposed a voxel-based method to extract

individual trees from mobile laser scanning data but

their method is not suitable for use with airborne Li-

DAR scans. Zhang et al. (2015) developed a method

to estimate tree metrics for urban forest inventory pur-

poses by detecting treetops and using a region grow-

ing algorithm for segmentation.

We propose a new technique for automatically de-

tecting trees in urban environments by using the num-

ber of returns information embedded in LiDAR data.

Similar to previous approaches, our method starts

with ground removal; however, following that step we

voxelise the point cloud data and show that trees can

easily be extracted from this subsampled data by us-

ing certain heuristics and data analysis.

Figure 2: Voxelized point cloud showing number of returns.

3 METHODOLOGY

Our method for labelling trees is based on four dis-

tinct steps: ground ﬁltering, voxelizing non-ground

point cloud data, isolating tree-like regions using the

information gained from the number of returns, and

post-processing to remove false positives.

3.1 Ground Filtering

A Digital Terrain Model (DTM) is used to represent

the surface of the Earth and there is a vast body of re-

search in extracting ground points from LiDAR scans

in order to produce a DTM. There are number of dif-

ferent algorithms for ground ﬁltering such as morpho-

logical ﬁltering, surface based adjustment and statis-

tical analysis (Chen et al., 2017).

We use a Progressive Morphological Filter

(PMF) (Zhang et al., 2003) to identify ground points.

PMF uses the morphological operations of dilation

and erosion, where it uses progressively increasing

window sizes to identify non-ground points. We ﬁlter

out the ground points identiﬁed using this technique

and the results are shown in Figure 3(b). However,

it is not successful in removing all the ground points,

hence we do statistical outlier removal on the ﬁltered

point cloud and get a much cleaner result can be seen

in Figure 3(c).

Automatic Tree Annotation in LiDAR Data

(a) Original Point Cloud

(b) Result of ﬁltering with PMF

Figure 3: Ground Filtering.

3.2 Voxelization

An aerial LiDAR survey returns an extremely large

volume of data, with an hour-long survey generat-

ing over a billion unique points (Geosystems, 2015).

Even after ﬁltering the ground points, the dataset can

retain more than half of the original points if the sur-

vey was in an occupied region, such as forests or ur-

ban areas. Hence, LiDAR point cloud data is often

converted to a mesh in order to reduce its dimen-

sionality. However, meshing algorithms can be error

prone when there are voids in the data since they make

assumptions about the shape in order to make water-

tight meshes. Meshing algorithms such as Poisson re-

construction require normals for the points which are

not directly available from the LiDAR output. They

also remove tall thin objects such as lampposts, tree

trunks and chimneys during the ﬁtting process.

We convert the data into a volumetric occupancy

grid in order to overcome the limitations of mesh-

ing algorithms while reducing its dimensionality. A

ﬁxed size 3 dimensional grid is overlaid on the point

cloud and the occupancy of each cell depends on the

presence of points within the cell, i.e. the cell is un-

occupied if there are no points in the cell’s volume

and vice versa. In this case, each volumetric ele-

ment, Voxel, represents a region in the subsampled

point cloud. Following the original tile dimensions of

100m × 100m, we convert each tile into a voxel grid

of dimensionality 256 ×256 × 256 hence limiting the

resolution of the voxel grid to be ≈ 0.39m × 0.39m ×

0.39m per voxel. Any further increase in resolution

does not noticeably increase accuracy and causes an

exponential increase in the processing time due to the

increased dimensionality of the data.

Furthermore, we use VOLA (Byrne et al., 2017)

to sparsely encode the voxel representation. VOLA is

a hierarchical 3D data structure which only encodes

for occupied voxels with a one bit per voxel using a

standard unsigned 64 bit integer. Unlike standard oc-

trees, which does not explicitly encode empty voxels,

we use a 2 bits per voxel approach to encode the ad-

ditional information per voxel such as colour, number

of returns and intensity value.

3.3 Isolating Tree Regions

LiDAR pulses reﬂect from from surfaces they en-

counter such as buildings, vegetation and ground.

Each pulse can return to the LiDAR sensor once or

multiple times, depending on the number of surfaces

it encounters. Trees typically have a high number of

returns since the laser pulses can reﬂect from multiple

edges of leaves and branches. Other features that can

have a high number of returns are the edges of build-

ing and window ledges. However, these latter values

are more scattered than in the case of trees which have

a large number of high returns closely packed as can

be seen in Figure 1.

We use this insight to isolate tree regions by iden-

tifying voxels with multiple returns (greater than 3 per

voxel) and then doing a connected component analy-

sis on these voxels. Regions with a minimum num-

ber of connected components are then identiﬁed as

tree regions, while any regions with smaller than the

threshold value are discarded as noise from buildings,

corners, etc.

GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management

3.4 Identifying Individual Trees

The tree regions isolated by using connected compo-

nents typically only return tree canopy as trunks might

have only a few disconnected voxels. Hence, in or-

der to ﬁnd tree trunks, the maximum and minimum x

and y coordinates of each region are identiﬁed, along

with the maximum z coordinate. These coordinates

are then used to place a three dimensional bounding

box in the original data which is extended to ground

level in order to capture the trunk information.

The width to length ratio of the bounding box is

constrained so that one dimension is never more than

twice the other. Any regions not matching these con-

straints are discarded as a false positive. This allows

to discard walls covered with ivy since walls are typi-

cally long but not very thick, whereas trees have sim-

ilar widths and lengths and hence the width to length

ratio ≈ 1.

(a) Experiment 1

(b) Experiment 2

Figure 4: Map of survey area with tree locations shown in

yellow and the outputs of our labelling algorithm shown in

red.

4 EXPERIMENTS AND RESULTS

4.1 Data

This method was tested on a dense LiDAR dataset of

Dublin city (Laefer et al., 2015). This dataset was

captured at an altitude of 300m using a TopEye sys-

tem S/N 443. It consists over 600 million points with

an average point density of 348.43points/m

. It cov-

ered an area of 2km

in Dublin city centre.

We tested our results with the labels from Ningal

(2012) containing tree annotations around some of the

major streets in Dublin from 2008. In order to get

more up to date results, we manually annotated the

region north of the Liffey river for trees.

4.2 Evaluation Metric

There are three different results for the purposes of

detection: True Positives (TP) where the trees are cor-

rectly recognised, False Positives (FP) where regions

are incorrectly identiﬁed as trees, and False Negatives

(FN) where trees are not detected. Based on these re-

sults we evaluate the following metrics (Goutte and

Gaussier, 2005):

r =

T P

T P + FN

× 100

p =

T P

T P + FP

× 100

score

= 2 ×

r × p

r + p

× 100

(1)

where r (recall) is the tree detection rate, p (precision)

is the tree detection precision and F

score

is the total

accuracy.

4.3 Results

The extracted trees have been shown in Figure 4 along

with the original labels. We compared our labelled

outputs with two different sets of annotations, the ﬁrst

from 2008 and the second from 2015 and the results

are summarized in Table 1.

Table 1: Summary of Results.

Experiment Trees TP FP FN p r F

score

1 313 178 45 135 0.57 0.8 0.66

2 535 469 56 66 0.88 0.89 0.88

The results of Experiment 1 (against 2008 anno-

tations) seem to suggest that the accuracy of our la-

belling method is extremely low with an F

score

0.66. On further analysis, we discovered that the ur-

ban landscape had changed a lot from when the tree

Automatic Tree Annotation in LiDAR Data

(a) Missed trees due to combined canopies

(b) Correctly labelled individual trees

Figure 5: Results.

labels were acquired in 2008 to when the LiDAR sur-

vey was done in 2015 due to roadworks and the con-

struction of the city tram. Hence, we annotated a sec-

tion of the city using imagery from 2015 to obtain a

fair analysis of our methods.

The results from the second experiment show that

our algorithm performs well by identifying almost

90% of the trees in Dublin correctly but has some

weak points. It is unable to distinguish between mul-

tiple trees packed closely as shown in Figure 5(a) and

assumes the entire canopy is a single tree leading to a

number of missed detections. Also, it mislabels heavy

ivy and bushes as trees since those produce multiple

returns as well. Our method performs extremely well

in labelling isolated trees irrespective of size, one of

such cases is shown in Figure 5(b), where all trees

along the side of O’Connell Street have been correctly

identiﬁed.

5 CONCLUSIONS

This paper addresses the challenge of automatic la-

belling trees in LiDAR data from urban environments.

Most previous work in this area focused on extract-

ing trees in forested regions, but the techniques can-

not directly be applied to urban environments due to

the complexity of the environment and the presence

of multiple objects.

The proposed method uses the number of returns

information present in the LiDAR data to isolate tree

regions and isolates individual trees by voxelizing

the data and ﬁnding clusters resembling trees using

connected component analysis. It deals well with

partially occluded trees and has achieved a satisfac-

tory accuracy of almost 90% in central Dublin hence

showing the effectiveness of the method. The method

has some drawbacks, namely that it is unable to sepa-

rate all individual trees within a large clump.

In order to deal with the current drawbacks, this

work can be extended in a number of ways in the fu-

ture:

• Improving individual tree detection in clumps by

isolating individual trunks along with the tree

canopy.

• Combining photogrammetry data with LiDAR

point cloud for more robust interpretation.

• Utilising the labelled trees from this method to

train a more robust machine learning based clas-

siﬁer which can generalise across point cloud

datasets for tree detection.

• Identifying building edges and windows using the

number of returns information and extrapolating

from those to obtain building reconstructions.

ACKNOWLEDGMENT

This research was undertaken while A. Gupta was an

intern at Intel Corporation and was partly funded by

the HiPEAC4 Network of Excellence under the EU’s

H2020 programme, grant agreement number 687698.

GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management

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Automatic Tree Annotation in LiDAR Data