J. H

1
Parameters in Airborne Laser Scanning (ALS)
ALS uses laserpulses to measure distances (slant
ranges) from the aircraft to the surface of the
earth. Together with a known (or measured) sweep
angle and position- and attitude measurements the
spatial co-ordinates of points at the terrain
surface can be derived. The Z-co-ordinate is
the value for a small area (footprint). Its size
depends on the beam divergence and the flying
height

J. Höhle Integration of Data in Mapping,
AAU-course 2007
2
Errors in Airborne Laserscanning
In the neighbourhood of houses the derived DTM
can have errors in position (?p) and elevation
(?h). These errors are caused by beam divergence
and multipath. The superimposition of a vector
map with the DTM reveals such errors.
Outliers may occur due to multipath. The
recorded laserpoint is below the terrain.
Filtering programs have to detect and eliminate
such outliers. Errors and gaps in the DTM will
remain.
J. Höhle Integration of Data in Mapping,
AAU-course 2007
3
Problems in DTMs from Airborne Laser Scanning
2D Plot
Many DTM posts are inside the houses. They were
not removed by the filtering process. Such
blunders can be automatically detected by means
of vector data.

Profile
J. Höhle Integration of Data in Mapping,
AAU-course 2007
4
Positional Errors in Airborne Laser Scanning
Positional errors can be detected by
superimposition of laser points on top of
orthophotos and vector map data or by an
isometric view.

Orthophoto with with laser points and map roof
line
Isometric view of laser points Their position is
visualized by colors. Points inside the house
should have been removed.
Map with roof line derived from laser points
J. Höhle Integration of Data in Mapping,
AAU-course 2007
5
Differences between Methods of Data Collection
2D Plot

Profile
Automated Photogrammetry
2.0 m
Laserscanning
Roof
Profile exaggerated
Terrain
Filtered away
0.2 m
Differences between Methods of Data Collection
J. Höhle The EuroSDR Project Automated  DTM
Checking - Goals and Expectations
J. Höhle Integration of Data in Mapping,
AAU-course 2007
6
Object-oriented classification
Objects and selected samples
Result of classification
7
Automated recognition of traffic signsan example
of object-oriented classification
Course Data integration for the 8th semester of
Chartered Surveyor study at AAU by J. Höhle

Source AAU project in Measuring science 2006,
group 8.2
8
Automated recognition of traffic signsby
object-oriented classification
source AAU-project 8.2, 2006
traffic sign C55
classes, sub-classes, attributes and thresholds
9
Automated recognition of traffic signsby
object-oriented classification
source AAU-project 8.2, 2006
Danish traffic signs
C55
E53
Design of a key for the two traffic signs C55
E53
10
Automated recognition of traffic signsby
object-oriented classification
source AAU-project 8.2, 2006
segmentation
classification-based segmentation
11
Automated recognition of traffic signsby
object-oriented classification
source AAU-project 8.2, 2006
12
Chartered surveyor study, 8th semester,
Measuring Science
SW solutions for Integrated Sensor Orientation
GPS Geonap IMU AEROoffice (IGI) Poseo
(Applanix) Aerotriangulation BINGO (Dr.
E. Kruck, Aalen) Match-AT, PAT-B (Inpho,
Stuttgart) Automated Aerotriangulation
(Z/I Imaging) BLUH (Dr. K. Jacobsen, Uni
Hannover)
J.Höhle Course Data integration, module 4
13
Chartered surveyor study, 8th semester,
Measuring Science
Integrated Sensor Orientation with BINGO
J.Höhle Course Data integration, module 4
14
Chartered surveyor study, 8th semester,
Measuring Science
Area B (KMS data Scanned contours) Method Two
orthoimages - P
delivered DTM derived corrections corrected DTM
Source EuroSDR publication 51
Result of the EuroSDR Test DTM Checking
J.Höhle Course Data integration, module 5
15
Chartered surveyor study, 8th semester,
Measuring Science
Leica ADS 40 Image GSD0.2 m
completeness 31, correctness 56 , RMSE1.5
pel
Aerial image (GSD0.5 m)
Source EuroSDR/Gerke 2006
Automated Extraction of Roads from Aerial Images
J.Höhle Course Data integration, module 5
16
Chartered surveyor study, 8th semester,
Measuring Science
IKONOS Image (GSD 1 m)
completeness 49, correctness 36 , RMSE1.8
pel
Source EuroSDR/Gerke, 2006
Automated Extraction of Roads from Satellite
Images
J.Höhle Course Data integration, module 5