Automated processing of point clouds to update land registry maps

Abstract

The quality control, maintenance, and renewal of land registry maps have always been priorities in the surveying profession. Many countries worldwide must face the issue that a significant part of their current digital land registry maps are based on old analogue maps that were digitised without involving any in-situ measurements. A direct consequence of this is that the digitised maps' accuracy leaves much to be desired and lags behind maps based on either correct survey or numerical data. Moreover, the quality of existing digital maps can be characterised by inhomogeneity that highly depends on the location. The final solution to the problem would be to carry out new surveys in the critical areas, but that has been postponed due to the lack of time and excessive costs.
However, in recent years, point cloud technologies, such as Unmanned Aerial Vehicles (UAV), Terrestrial Laser Scanners (TLS), Aerial Laser Scanners (ALS), together with Mobile Mapping Systems (MMS), have become the focus of attention in mapping. Thanks to these technologies, experts can survey large areas with the necessary and homogenous accuracy, high resolution, and significantly, very rapidly. It is beyond doubt that these modern technologies benefit the process of updating old and less relevant maps.
Another underlying aspect worth considering is the automation in data processing since a massive amount of data needs to be evaluated. Some algorithms and their validation on study areas in Hungary are presented in this paper. Our study focuses on the mapping of buildings using point clouds generated from UAV images.

Full text

Presenter: Bence Péter Hrutka (PhD Student)
hrutka.bence@emk.bme.hu
Automated processing of point clouds
to update land registry maps
Bence Péter Hrutka, Bence Takács, Zoltán Siki
Budapest University of Technology and Economics
Faculty of Civil Engineering
Department of Geodesy and Surveying
30.09.2021.

Introduction
•Purpose
•Measurements
•Processing
•Pre-process
•Possibilities of map creation
•Results
•Summary
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2

Primary issue in land registry
•Analogue maps
•Lenient regulations
•Lack of control
•Digitalisation
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3

Solution –new measurements
•New technologies
•UAV
•TLS
•ALS
•MMS
•Several examples
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4

Test measurements
•Two test areas:
•Barnag (38 hectares)
•Üllő (5.5 hectares)
•Significant differences
•Lenient regulation
•Allowed ~3.8m!
•Map renewal is warranted
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5
Barnag
Üllő

2017. 12. 14.
6
Barnag test area

Measurements - Barnag
•Photogrammetry
•DJI Phantom 4 Pro (20 Mpixel)
•38 hectares
•1000 images
•Flight altitude: 55 -70 m
•Oblique –25°
•GSD = 1.5 cm/px
•RTK-GNSS
•Ground Control Points (GCPs)
•Georeferencing
•Results
•True-orthophoto
•Point cloud (~450 million points)
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7

Measurements - Üllő
•Photogrammetry
•DJI Phantom 4 Pro
•5.5 hectares
•805 images
•Flight altitude 50 m
•Oblique –25°
•GSD = 1.4 cm/px
•RTK-GNSS
•6 GCPs
•Results
•True-orthophoto
•Point cloud (~105 million points)
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8

Control measurements
•RTK-GNSS
•Control points
•Total station
•Validation points
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Point cloud creation –ODM
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10

Point cloud pre-processing - nDSM
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11
Mesh generation
Difference in
elevation
Point cloud
classification
Point cloud Off-ground
points
Ground points Mesh of ground
points
nDSM

Point cloud pre-processing - segmentation
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12
Normal based
segmentation Segmented
points with
horizontal normal
nDSM
……

Point cloud pre-processing - segmentation
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13
Region
growing
Point cloud of
segmented wall
points
…Segmented
points with
horizontal normal

Point cloud processing
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14
nDSM
Mesh generation
Difference in
elevation
Point cloud
classification
Input point cloud
Off-ground
points
Ground points
Mesh of ground
points
Normal based
segmentation+
Z koord. alapján
Segmented
points with
horizontal normal
Region
growing
Point cloud of
segmented wall
points
Robust linear
regression
Sequential
RANSAC
Raster-vector
conversion

Results
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Robust linear
regression
Sequential RANSAC
Raster –vector conversion

Results - comparison
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16
Statistical measure of the results in the
Üllő test area [cm]
Linear
regression
Sequential
RANSAC
Raster-
vector
conversion
Average 9 9 18
Minimum
1 0 4
Maximum
26 33 48
Median 7 8 15
Std. Dev. 7 7 12
•41 validation points
•Robust linear regression -35
•Sequential RANSAC - 26
•Raster-vector conversion - 35
•Results of manual processing:
•Std. Deviation of 5 -15 cm

Summary
•Primary issue in the land registry
•A Hungarian campaign
•Processing
•Pre-process
•3 algorithms:
•Robust linear regression
•Sequential RANSAC
•Raster to vector conversion
•Results
•Comparison
•Further possibilities
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17

Thank you!
Köszönöm szépen!