EuroSDR Projects - Host Ireland

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<strong>EuroSDR</strong>-Project Commission 3 “Evaluation of Building Extraction” Final Report Report by Harri Kaartinen and Juha Hyyppä Finnish Geodetic Institute, Masala, Finland
Page 1 and 2: European Spatial Data Research Nove
Page 3 and 4: PRESIDENT 2006 - 2008: Stig Jönsso
Page 5 and 6: H. Kaartinen and J. Hyyppä: EVALUA
Page 7 and 8: 4.3.1 Data and study area..........
Page 9: 2 QUESTIONNAIRES...................
Page 14 and 15: Due to the development of scanning
Page 16 and 17: (from 1.6 to about 20 pulses per m
Page 18 and 19: 16 Figure 2-5: Espoonlahti test sit
Page 20 and 21: 2.1.3 IGN Test Site Amiens This stu
Page 22 and 23: 2.3 Produced 3D-models 3D-models we
Page 24 and 25: Figure 3-1 shows the work- and data
Page 26 and 27: calibration and exterior orientatio
Page 28 and 29: Figure 3-6: Breaking down a complex
Page 30 and 31: In TerraScan the operator can selec
Page 32 and 33: 3.1.2.6 IGN IGN used calibrated aer
Page 34 and 35: 32 Figure 3-15: The roof segments.
Page 36 and 37: 34 Preparation Building Polygons, T
Page 38 and 39: Normal: Manual task of the operator
Page 40 and 41: 38 Figure 3-23: A building’s poin
Page 42 and 43: 3.1.4 Level of Automation 3.1.4.1 S
Page 44 and 45: 3.1.4.9 Delft University of Technol
Page 46 and 47: 44 Test site Participant Espoonlaht
Page 48 and 49: 46 CyberCity Stuttgart IGN Delft IC
Page 50 and 51: 48 IGN Nebel+Partner C+B Technik 4.
Page 52 and 53: Height Cyber- City Hamburg Stuttgar
Page 54 and 55: Height Cyber- City Stuttgart IGN IC
Page 56 and 57: Height Cyber- City Stuttgart IGN IC
Page 58 and 59: Height Cyber- City HamburgStuttgart
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58 Height CyberCity N 40 Min 11 Max
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60 IQR [m] IQR [m] IQR [m] 1.2 1.0
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62 Building outline deviation IQR [
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IQR [m] 64 2.5 2.0 1.5 1.0 0.5 0.0
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All Cyber- ICC laser+ Nebel+ C+B FO
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4.2.3 Building Length Building leng
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[m] [m] 70 1.8 1.6 1.4 1.2 1.0 0.8
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[degrees] 72 [degrees] 6.0 5.0 4.0
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74 Total relative shape dissimilari
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Acknowledgments All participants, i
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Centeno, J.A.S. and Miqueles, J., 2
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Haithcoat, T.L., Song, W. and Hippl
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Oda, K., Takano, T., Doihara, T. an
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Vosselman, G. and Süveg, I., 2001.
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Figure 4-6: Building outline deviat
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Appendix 1: Test Site Visualization
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Wireframe models of Espoonlahti, Se
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Hermanni by IGN, with and without t
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Amiens by IGN, with and without tex
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CyberCity Hamburg ICC laser+aerial
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CyberCity Hamburg ICC laser+aerial
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Espoonlahti Senaatti Amiens Hermann
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104 Laser data only: ICC laser, FOI
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106 Laser data only: ICC laser, Aal
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108 Laser data only: ICC laser, FOI
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110 Laser data only: ICC laser, Aal
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Abstract This report describes a me
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Object based classifiers, such as e
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Even though a rule based system is
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Country Number of classes Single bu
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Figure 4-3: Digital cadastral map o
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4.1.3 Classification Figure 4-6: Se
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Figure 4-7: Classification of 1994
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Figure 4-9: Result of evaluation of
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DCM 2004 DCM 1994 Agriculture Build
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4.2 Switzerland 4.2.1 Data and Stud
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4.2.2 Segmentation, classification
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Figure 4-15: Classification of Swis
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4.3 Germany 4.3.1 Data and study ar
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Land use Classification Forest Mead
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the archive have been selected for
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Figure 4-23: ATKIS data: green = gr
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German method. Since eCognition is
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Land use Classification Forest Mead
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Figure 4-29: Ultracam false-colour
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Figure 4-33: Classification of real
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The classification results were com
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Figure 4-40: Subset of change map f
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References BAATZ, M., SCHÄPE A., 2
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Change Detection Workshop 16.-17.6.
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Feedback will be given at next work
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Object-Oriented Classification of O
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Table 3 gives an overview over the
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data were selected as they contain
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Change Detection Workshop 26.10.200
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additional tests and/or adjustment
Page 183 and 184:
Projekt „Change Detection“ z.H.
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EuroSDR-Project Commission 2 “Ima
Page 188 and 189:
1.2 Contest Phases In phase 1 test
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188 Figure 1: Data set Copenhagen,
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The pictures show the same display
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3 Reference Data In order to have a
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a) c) 194 Copenhagen Fjärdhundra T
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Figure 9 shows one example of image
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To obtain standardised values for t
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200 160,0 140,0 120,0 100,0 80,0 60
Page 204 and 205:
5.2 Test Site Fjärdhundra 5.2.1 Ar
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5.2.2 Qualitative Comparison Simila
Page 208 and 209:
206 140,0 120,0 100,0 80,0 60,0 40,
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Detection of buildings was not a bi
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210 120,00 100,00 80,00 60,00 40,00
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6 Conclusion - Phase I The results
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Index of Figures Figure 1: Data set
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EuroSDR-Project Commission 2 “Ima
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have developed semi-automated tools
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GDF includes topology, road classes
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cautiously than the rest. Two group
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optimize their parameters for each
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228 road cluster is refined by remo
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5 Analysis of Results We focus the
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232 Figure 4: Ikonos1 - Gerke_W (le
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• Ikonos3_Sub1 and Sub2: These tw
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236 Figure 9: Ikonos3_Sub2 - Bacher
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approaches. Unfortunately just obta
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Bacher, U. and Mayer, H. (2005): Au
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Appendices Appendix 1: Project Prop
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Implementation The project should b
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Brown University (Cooper), Univ. of
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Appendix 2: Questionnaire for Produ
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All spectral channels used: yes n
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14. How are road classes defined?
Page 257:
C. Customers and applications List
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General information Corresponding P
Page 262 and 263:
5. How is the smoothness of road tr
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DSM from LIDAR for segmentation of
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- DTM as typically existing in Euro
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no [4] Remark: could be useful to g
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Optimal exploitations in practical
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Appendix 5: README File EuroSDR Tes
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- percentage correctness - geometri
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Appendix 7: Documentation by Hedman
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polyline formation process. Only th
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Landesvermessungsamt Baden-Württem
Page 286:
45 Murray, K. (ed.): OEEPE Workshop
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