Synergy of LIDAR and High-Resolution
Digital Orthophotos to Support Urban
Feature Extraction and 3d City Model
Construction
Fang Qiu
Geospatial Information Sciences
The University of Texas at Dallas
Introduction
‹
Digital city models (DCMs) comprise 2D and 3D
geometric and geomorphic information of important
urban features, such as buildings, streets, trees and
underlying terrain
‹
Accurate digital city models in urban areas are essential
for many applications
‹ Urban
and environment planning, transmitter placement for
telecommunication, location based services, navigation and
simulations, microclimate investigations and many more
‹
Digital orthophotography is an established technology
for mapping urban feature.
‹
LIDAR (Light Detection and Ranging) is a relatively new
technology for obtaining digital elevation model
Dr. Fang Qiu
Problems with LIDAR and digital orthphotgraphs
‹
The most accurate method of building extraction from
orthophotos is through heads-up digitization and softcopy photogrammetry
‹ High
spatial resolution and rich spectral information provide
good differentiation of various urban features.
‹ Unfortunately,
very expensive, labor-intensive and time
consuming. Too few points lead to estimation to fill the gap.
‹
LIDAR data is not selective, consisting of a tremendous
number of points returned from all possible reflective
objects
‹ The
process to separate urban features to build digital city
models from the dense points cloud is a rather difficult process.
‹ Unwanted
small surface objects (power lines, automobiles, etc),
and roof top objects (spires, air conditioning units, etc) need to
be removed to obtain correct building models.
Dr. Fang Qiu
Research Objectives
‹
Examine the applicability of LIDAR data for creating 2D
and 3D city models
‹
Investigate the differentiation of buildings from other
features using orthophotos in the urban areas.
‹
Determine if the fusion of the elevation information from
LIDAR with the spectral information from digital
orthophotos
‹ Could
compensate the shortcomings of each and take the
advantages of both
‹ So
that it is possible to improve building extraction for creating
highly detailed digital city models.
‹ Establish
an automated approach to accurate DCM construction
that is both less expensive and less time-consuming
Dr. Fang Qiu
Background
‹
Processing raw points clouds first by applying various
vector filters and then interpolating the points to form
DEM of different feature types.
‹
Interpolating raw point clouds to derive DEM first and
then processing the DEM to generate detail DCMs
‹ We
choose the second approach because DEM has a compatible
data structure with digital orthophotos.
Dr. Fang Qiu
Airborne LIDAR
‹
A laser altimeter is operated from an airplane, or a
helicopter
‹
The instrument emits laser pulses which travel to
the surface, where they are reflected
‹
Part of the reflected radiation returns to the laser
altimeter, is detected, and stops a time counter.
‹
The distance to the Earth's surface is determined
by measuring the time-of-travel of a short flash of
infrared laser radiation
‹
The exact 3D geographic coordinates (latitude,
longitude, elevation) is derived by merging laser
ranging, Global Positioning System (GPS), and
Inertial Navigation System (INS).
Dr. Fang Qiu
Terminology
‹
Reflective LIDAR points: Discrete (vector) elevation point clouds at a
certain spaced intervals representing full recording of all the reflections
from the surface including vegetation, buildings and other objects.
‹
Bare-earth LIDAR points: recordings of the pure bare-earth surface where
above ground features (buildings, vegetation and others) are removed
‹ The Digital Elevation Model (DEM): a continuous (raster) representation
describing the shape of the surface where elevation is a function of latitude
and longitude. Two types of DEMs are useful:
‹ DSM - Digital Surface Model is an elevation model which describes the earth's
surface including buildings, vegetation and other objects. It contains elevation
values of the earth's surface as it is and can be generated directly from reflective
LIDAR points
‹ DTM - Digital Terrain Model is an elevation model which depicts the pure terrain
surface without buildings and vegetation in a way terrain elevation is given in
topographic maps. It is derived from the Bare-earth LIDAR points or from DSM by
applying filtering functions which remove surface objects.
Dr. Fang Qiu
Study Area
‹
Downtown Dallas, TX with nearby residential areas
‹Approximately 16.8 square miles
Dr. Fang Qiu
Data Sources
LIDAR
Digital Orthophotos
‹
Altitude of capture: 8,000’
‹
Capture Period: Nov, 00-Jan01
‹ 6”
‹
Point Spacing: 5 meter (16 feet)
‹12”
‹
Vertical accuracy: 15-18 cm or
6-7 feet
‹
Both reflective and bare-earth
data points are available
Dr. Fang Qiu
‹
Altitude:
focal length: 4,500’
focal length: 9,000’ (Down
area)
‹
Capture period: Jan. 2003
‹
Ground Resolution: 0.5 feet
‹
Color Type: 24-bit natural color
LIDAR data preprocessing- generate DEMs
LIDAR ASCII Files
3D Points
Visualization
TIN
Point Feature Class
DEM raster from interpolation (IDW)
Slope
Dr. Fang Qiu
Aspect
Shaded Relief
3D Grid
Visualization
3D TIN
Visualization
LIDAR data preprocessing
3D Points cloud
3D TIN
3D DSM
Dr. Fang Qiu
2D and 3D Building Extraction
‹
2D Model
‹ Detect
and isolated building features from trees using LIDAR or
digital Orthophoto
‹ Obtain
horizontal position and shape information for the
detected building features
‹
3D Model
‹ Measure
the height information for the buildings detected
‹ Determining
building roof types based on roof slope
‹ Attached
height to the 2D building models to create 3D digital
city models
Dr. Fang Qiu
2D Building Extraction from normalized DSM
‹
Normalized DSM (NDSM)
‹
Height thresholding
DSM
DTM
NDSM
‹
Region grouping
‹
Area threshoding
‹
Raster to vector conversion
‹
Manually remove long features
‹
Polygon generalization
Manually remove long features (highways)
‹
Polygon simplification
Polygon simplification
Dr. Fang Qiu
Height Thresholding
Region grouping
Area Thresholding
Raster to vector conversion
Normalized Digital Surface Model (NDSM)
‹
Normalized DSM (NDSM)
‹
Difference model between DSM and DTM
Š NDSM = DSM –DTM
‹
All objects in the NDSM stand on surface of zero elevation (Vozikis, 2004)
‹
The influence of terrain topography is excluded
‹
NDSM provides an initial buildings and trees segmentation from terrain surface
Dr. Fang Qiu
Building Detection from NDSM by Height Thresholding
A subset of the building mask
‹
Buildings are isolated based on a height threshold of 8 ft
to mask out lower trees and other ground objects
‹
Con (NDSM < 8, NODATA, 1)
Dr. Fang Qiu
Building Detection from NDSM by Area Thresholding
‹
An industrial area
‹
No large trees in
this area
‹
‹
Dr. Fang Qiu
Regiongroup to
generate unique
regions
Use area threshold
to remove small
objects
‹
Convert to vector file
‹
Apply Douglas and
Peucker simplification
Problems with Building Extract from NDSM
‹
Large trees that are connected with each other were
confused with buildings
‹
This is especially a problem in established residential
area where large threes can be taller than houses
Dr. Fang Qiu
Building Extraction from Digital Orthophotos
‹
An object-oriented image classification method was
used. The basic processing units of object oriented
classification are image objects or segments, rather
than single pixel – feature extraction
‹
The method can utilizes spectral, spatial, texture,
context and other ancillary information to model the
feature extraction process
‹
The results are the creation of image objects defined
as individual areas with shape and spectral
homogeneity (Jensen, 2005)
‹ Feature
Analyst™ - An ArcGIS Extension was used to
implemented the object-oriented image classification
method.
Dr. Fang Qiu
Feature Extraction Using Object-oriented Approach
‹
‹
‹
An input representation is a method that
takes into account the spatial context of a
pixel
This is done with two different adjustable
settings, Pattern Type and Pattern Width.
Ten different possible search patterns are
provided.
Option
Common Uses
Bull’s Eye 1
The Bull’s Eye options are used on Rivers, roads
and Small features. Pattern width depends on image
resolution.
Bull’s Eye 2
Bull’s Eye 3
Bull’s Eye 4
Square
Circle
Manhattan
Vegetation, lakes, land cover (3x3), buildings (5x5)
• This pattern focuses on texture
• Preserve the lines between the adjacent features
• Good for extracting feature that have varying
spatial components
User-defined Foveal
User-defined Foveal
Dr. Fang Qiu
The pixel at the center of the grid
represents the decision pixel
Ignored pixel
High Rise Buildings in Densely Built-up Area
Orthophoto
‹
Buildings Footprints
Object oriented classification of the
orthophoto
Image classification using objected-oriented approaches
‹ Provide
much better separation between buildings and trees
‹ The
extraction of high rises building failed due to effects of
occlusion and shadow
Dr. Fang Qiu
Houses and Trees in Residential Area
‹
Extracting of houses with limited success, but parking
lots and streets are misclassified as buildings
‹
Trees are confused with grass in many areas
Dr. Fang Qiu
Feature Extraction Based on Fussing LIDAR Data and
Digital Orthophotos
‹
‹
Image classification using fused data combining LIDAR
and orthophotos with object-oriented image
classification approach
‹
The NDSM included as “pseudo” band and combined with
‹
The three spectral bands of the orthophotos.
Feature extraction performed in the following sequence
1.
Trees was extracted first and then masked out
2.
Then high rise buildings was extracted and masked out
3.
Finally, residential houses and low rise buildings were
extracted.
Dr. Fang Qiu
Tree extraction based on fussing LIDAR data and
Digital Orthophotos
Tree extracted from the NDSM and the orthophoto
‹
Trees are not confused with grass any more
‹
The resulting trees are mask out to improve subsequent building
extraction
Dr. Fang Qiu
High Rise Building Extraction Based on Fussing
LIDAR Data and Digital Orthophotos
‹
Classification of the
orthophoto without
incorporating
elevation data failed
in densely built up
areas due to the
occlusion and
shadow
Dr. Fang Qiu
‹
Buildings extraction
from the NDSM
‹
Trees and building
can not be effectively
differentiated
‹
Building extraction
using fused information
‹
Manhattan 5 input
representation was used
‹
An area threshold was
applied to remove
objects with area less
than 300m²
High Rise Building Extraction: Generalizing and
Smoothing
Dr. Fang Qiu
‹
Post processing was then
performed using Douglas and
Peucker simplification
‹
Area threshold was applied
‹
The results were improved
dramatically.
High Rise Buildings Extraction in a Densely Built
Up Area
Orthophoto
Original buildings Footprints
Object oriented classification of the
orthophoto. The effect of shadow is very
clear
Dr. Fang Qiu
Object oriented classification of the
combination of the orthophoto and the
normalized DSM. The effect of shadow is
eliminated
The NDSM reclassified and vectorized. Trees
are classified as buildings
Post processed buildings
Residential Houses and Low Rise Buildings: First Pass
and Identify Correct and Incorrect samples
Houses and small buildings extraction results first pass
Refining the results by selecting correct and incorrect examples
‹
With the fused information from orthophotos and NDSM, houses
and low rise buildings are extracted in two passes
‹
Manhattan 5 input representation was used in the first pass
‹
An area threshold was applied to remove objects with area less than
150 m²
Dr. Fang Qiu
Second Pass and Post Processing
‹
Bull’s Eye 4 with pattern width 27 was used in the second pass to remove
unwanted objects based on the correct and incorrect examples
‹
Douglas and Peucker generalization method were applied to simplify and
smooth the results
‹
The results were improved dramatically and adjacent houses that were
classified as one in the first pass were separated in the second pass.
Dr. Fang Qiu
Comparison of the Three Methods
Dr. Fang Qiu
‹
Use NDSM only, trees are
confused with buildings.
‹
Use spectral bands only,
buildings are confused with
parking lots and road.
‹
Using the fused information,
buildings are well separated
from trees and parking lots.
Accuracy Assessment
Building
Extraction
Method
Type
Dr. Fang Qiu
Original
buildings
foot prints
Buildings
extracted
based on
and
Fussing
LIDAR NDSM
and digital
orthophots
Digitized
buildings
and houses
%
High rise
Buildings
500
469
93.8%
Houses
and small
buildings
416
351
84.4%
Total
916
820
89.5%
‹
In the downtown area the original
building foot prints were used
‹
In other areas, some buildings and
houses were digitized from the
orthophotos
Roof Classification and Measuring Building Height
‹
Building roof can be classified into
two types based on the roof slope
derived from NDSM
‹Flat
roof: slope <=20o
‹Sloped
‹
roof: slope > 20o
Measuring Building Height
‹Flat
roof: Small objects exists on top of
the roof such as spires, antennas, air
conditioning units, heating systems, etc.
Their heights should be removed to get
an accurate buildings height measuring.
‹Sloped
roof: The highest portion of the
buildings is the ridgeline of the roof.
Dr. Fang Qiu
Roof Classification based on Slope of NDSM
Dr. Fang Qiu
Roof Classification based on Slope of NDSM
‹
In the study area 128 buildings were selected to evaluate the accuracy of roof types
and 109 buildings were classified correctly either to flat or sloped roof
‹
The accuracy is 85.16%
Dr. Fang Qiu
Buildings heights
‹
Obtaining the Buildings Heights From the NDSM:
‹ Sloped
roof buildings: the heights of the sloped roof buildings
were obtained from the NDSM using Zonal maximum, because
the highest section of the buildings is the ridgeline of the roof.
‹ Flat
roof buildings: the heights were obtained from the NDSM
using zonal majority to remove the affects of building’s tops
objects.
‹
Accuracy assessment:
‹ was
performed on the buildings extracted based on fused
information
‹ The
original buildings heights were compared with those
obtained using the zonal statistics functions.
Dr. Fang Qiu
Building Name
Fountain Place
Belo Building
One Bell Plaza
Commerce Building
Santa Fe Building
Trammell Crow Tower
Hyatt Regency Reunion Hotel
Main Tower
The Metropolis
One Main Place
Fidelity Union Tower
Plaza of the Americas I
Adam's Mark Hotel
Adolphus Hotel
JP Morgan Chase Tower
Harwood Center
Bryan Tower
Tower Petroleum Building
Thanksgiving Tower
Plaza of the Americas III
Hampton Inn West End
1700 Commerce Place
Lincoln Plaza
Manor House
San Jacinto Tower
1600 Pacific Building
Dallas Grand Hotel
KPMG Center
SW Bell 311 S Akard
Fairmont Hotel
Elm Place
Bank one Center
One Dallas Center
Energy Plaza (Antenna)
SW Bell 308 S Akard
Republic Center Tower (Antenna)
Bank of America Plaza
Magnolia Hotel (Antenna)
St. Paul Tower
Mercantile National Bank (Antenna)
1700 Pacific Avenue
Renaissance Tower (Antenna)
Roof Type
Sloped
Sloped
Sloped
Sloped
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Flat
Dr. Fang
Qiu
Flat
Actual Building Height (ft)
720
377
580
360
300
686
345
336
303
445
400
341
352
312
738
483
512
315
645
341
268
224
579
319
456
434
234
481
325
308
625
787
448
629
325
598
921
430
268
523
655
886
LIDAR Height (ft)
719.86
376.78
579.08
359.03
299.01
684.9
343.74
334.65
301.26
443.03
398.01
339
350
309.79
735.58
480.35
509.09
311.82
641.67
337.54
264.35
220.26
574.18
314.13
451.09
429.96
229.89
476.85
320.77
303
619.89
781.76
442.7
623.11
318.82
591.31
914.08
422.67
260.19
515.05
647.01
875.3
Height Difference
0.14
0.22
0.92
0.97
0.99
1.1
1.26
1.35
1.74
1.97
1.99
2
2
2.21
2.42
2.65
2.91
3.18
3.33
3.46
3.65
3.74
4.82
4.87
4.91
4.04
4.11
4.15
4.23
5
5.11
5.24
5.3
5.89
6.18
6.69
6.92
7.33
7.81
7.95
7.99
10.7
Dr. Fang Qiu
Summary
‹
This research focused on the extraction of urban features (e.g. buildings and
trees) and construction of DCMs on the basis of synergism of LIDAR and
digital orthophotos. For urban feature extraction, three methods were
compared and applied in the city of Dallas:
‹
The analysis of LIDAR data to derive urban features using normalized digital surface
model;
‹
The object oriented image classification of high spatial resolution digital
orthophotos
‹
The fusion of spectral information from digital orthphotos with the normalized
digital surface model (NDSM) drawn from LIDAR data.
‹
It was demonstrated that the third method built upon the integration of LIDAR
and digital orthophotos data greatly improved the accuracy of urban feature
extraction.
‹
To construct DCMs, we first classified the buildings to those of flat roof and
sloped roof using the roof slopes derived from LIDAR NDSM.
‹
The heights of flat roof and pitched roof buildings were then calculated using
zonal majority and zonal maximum, respectively, where the zones are
obtained from the last feature extraction process.
Dr. Fang Qiu
References
Aeromap, http://www.aeromap.com/lidar_basics.htm, last visited date September 16, 2005
Abo Akel, N., Zilberstein, O. and Doytsher, Y. 2003, “Automatic DTM Extraction from Dense Raw
LIDAR Data in Urban Areas,” FIG Working Week 2003 Paris, France, PP26.1 April 13-17, 2003.
Ahlberg, S., Söderman, U., Elmqvist, M. and Persson Å., 2004, “ON MODELLING AND
VISUALISATION OF HIGH RESOLUTION VIRTUAL ENVIRONMENTS USING LIDAR DATA,” Proc.
12th Int. Conf. on Geoinformatics − Geospatial Information Research: Bridging the Pacific and
Atlantic University of Gävle, Sweden, 7-9 June 2004.
Blundell, S., Finlayson, C., Opitz, D., 2004, “Modeling Buildings for Mission Rehearsals,”
Proceedings of ESRI professional papers, paper 1520, ESRI International User Conference, San
Diego, CA, 2004.
Briese, Ch., Pfeifer, N., Dorninger, P., (2002), “Applications of the Robust Interpolation for DTM
Determination,” Institute of Photogrammetry and Remote Sensing, Vienna University of
Technology, Gusshausstrabe 27-29. Commission III WG III/3.
Brovelli, M.A., Cannata, M. and Longoni, U.M. (2002), “Managing and Processing LIDAR Data within
GRASS,” Proceeding of the Open source GIS – GRASS users' conference- Italy.
Brunn, A., Weidner, U., 1997, “Extracting buildings from digital surface models,” In: IAPRS XXXII / 34W2, pp. 27-34.
Chen, L. C., Teo, T. A., Shao, Y. C., Lai, Y. C., and Rau, J. Y., 2004, “Fusion of LIDAR Data and Optical
Imagery for Building Modeling,” International Archives of Photogrammetry and Remote Sensing,
Vol. 35, No. B4, pp. 732-737.
Elaksher, A.F. and Bethel, J.S., 2002, “Reconstructing 3D buildings from LIDAR data”. Proceedings
of Photogrammetric Computer Vision, ISPRS Commission III, Symposium 2002, September 9 13, 2002, Graz, Austria, pp102-107.
Fowler, R., 2001, Topographic LIDAR. In: D. F. Maune (Ed), "Digital Elevation Model Technologies
and Applications: The DEM Users Manual.” American Society for Photogrammetry and Remote
Sensing, Bethesda, MD, Chapter 7: pp.207-236.
Dr. Fang Qiu
References (Cont.)
GeoLas Consulting, http://www.geolas.com/Pages/laser.html, , last visited date November 23, 2005.
Hu, J., S., You, U., Neumann 2000, “Approaches to Large-Scale Urban Modeling,” IEEE Computer
Graphics and Applications, Volume 23, Issue 6, pp. 62 - 69.
Huber, M., Schickler, W. and Hinz, S. Baumgartner, A., 2002, “Fusion of LIDAR Data and Aerial
Imagery for Automatic Reconstruction of Building Surfaces”; In: Proceedings of the IEEE/ISPRS
joint Workshop on "Remote Sensing and Data Fusion over Urban Areas,” Berlin, 2003; IEEE
Piscataway.
Jensen, John R., 2005, “Introductory Digital Image Processing,” 3rd Ed., Upper Saddle River, NJ:
Prentice Hall, 526 pages.
Kraus, K., Pfeifer, N., 1998, “Determination of terrain models in wooded areas with aerial laser
scanner data,” ISPRS Journal of Photogrammetry and Remote Sensing 53 (4), pp. 193-203.
Kraus, K., Pfeifer, N, (2001), “Advanced DTM Generation from LIDAR data,” IASPRS, Vol. XXIV–
3/W4, Annapolis, MD.
H. Masaharu, and K. Ohtsubo (2002), “A Filtering Method of Airborne Laser Scanner Data for
Complex Terrain,” Commission III, Working Group III/3.
Herold, M., Gardner, M. E. and Roberts, D. A. 2003, “Spectral resolution requirements for mapping
urban areas,” IEEE Transactions on Geosciences and Remote Sensing, accepted for publication.
Merrick & Company, http://www.merrick.com/servicelines/gis/lidar.aspx, last visited date October
17, 2005
Morgan, M. and Tempfli, K., 2000, “Automatic Building Extraction from Airborne Laser Scanning
Data,” International Archives of Photogrammetry and Remote Sensing, Amsterdam, 2000, 33(B3):
616-623.
N. Haala, C. Brenner, and K.-H. Anders, “3D urban GIS from laser altimeter and 2D map data,”
International Archives of Photogrammetry and Remote Sensing, vol. XXXII, part 3/1, pp. 339-346,
1998.
Dr. Fang Qiu
Reference
O’Brien, M.A., 2003, “Feature Extraction with the VLS Feature Analyst System”. Proceedings of the
American Society for Photogrammetry and Remote Sensing. NIMA/IDR, Reston, VA.
http://featureanalyst.com/feature_analyst/publications/reviews/obrien_asprs.pdf
Pfeifer, N., Stadler, P., Briese, Ch., 2001: Derivation of digital terrain models in the SCOP++
environment. Proceeding of OEEPE Workshop on Airborne Laserscanning and Interferometric SAR
for Detailed Digital Terrain Models, Stockholm, Sweden.
Ribarsky, W., T. Wasilewski, and N. Faust (2002), “From Urban Terrain Models to Visible Cities,” IEEE
Computer Graphics & Applications, Vol. 22.
Rottensteiner, F. and Briese, C., 2002, “A New Method for Building Extraction in Urban Areas from
High-Resolution LIDAR Data,” Symposium der ISPRS-Comm. III, Graz, 9 - 13 September 2002.
International Archives of Photogrammetry and Remote Sensing, Volume XXXIV / 3A, pp. 295 - 301.
Rottensteiner, F., 2003, “Automatic generation of high-quality models from LIDAR data,” Computer
Graphics and Applications, IEEE, 23(6), pp. 42-50.
Schenk, T. and Csatho, B., 2002, “Fusion of LIDAR data and aerial imagery for a more complete surface
description,” IAPRSIS, Vol. XXXIV/3A, Graz, Austria, pp. 310 – 317.
Wikipedia, http://en.wikipedia.org/wiki/Object_Oriented_Image_Classification, last visited date October
06, 2005
Voegtle, T., Steinle, E., 2003, “On the quality of object classification and automated building modeling
based on laser scanning data,” In: IAPRSIS XXXIV / 3W13, pp. 149-155.
Vozikis, G., 2004, "Urban Data Collection: An Automated Approach in Remote Sensing," Presentation:
24th Urban Data Management Symposium, Information Systems and the Delivery of Societal
Benefits, Chioggia, Venice; 10-27-2004 - 10-29-2004; in: "Proceedings of UDMS'04", (2004), 10
pages.
Vozikis, G., 2004, “Automated Generation and Updating of Digital City Models using High-Resolution
Line Scanning Systems,” 20th ISPRS Congress, Istanbul. In: International Archives of
Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 35 (and on DVDrom)
Dr. Fang Qiu