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European Spatial Data Research Nove
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PRESIDENT 2006 - 2008: Stig Jönsso
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H. Kaartinen and J. Hyyppä: EVALUA
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4.3.1 Data and study area..........
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2 QUESTIONNAIRES...................
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Abstract The objective of the EuroS
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The analysis of the structural qual
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Figure 2-3: Hermanni test site. Fig
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Espoonlahti Hermanni Senaatti Photo
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2.2 Reference Data 2.2.1 Field Meas
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Used data Time use Laser Aerial Gro
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Step 3: Import to CCModeler Figure
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Figure 3-4: Sequence of manual phot
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Figure 3-8: Workflow of laser scann
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Figure 3-11: Parametric building mo
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Each group of connected pixels clas
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Figure 3-18: Topological points and
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Detailed Description (Letters refer
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assumption is made: the two longest
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Figure 3-25: A 3D building model wi
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ICC used the same methods as Nebel
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where p75th is the value at the 75
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CyberCity Stuttgart Hamburg IGN ICC
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CyberCity Hamburg Stuttgart IGN ICC
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Height Cyber- City Hamburg Stuttgar
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Height Cyber- City Stuttgart IGN IC
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Height Cyber- City Stuttgart IGN IC
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Height Cyber- City HamburgStuttgart
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Height Cyber- City HamburgStuttgart
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All targets Eaves Ridges Height IGN
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CyberCity achieved a good quality i
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Point density, shadowing of trees a
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Height accuracy IQR [m] 1.6 1.4 1.2
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All Cyber- Ham- ICC laser+ Nebel+ I
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All Cyber- HamStutt- ICC laser+ Neb
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In building length determination (F
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degrees, std 6.3 degrees). In Senaa
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5 Discussion and Conclusions It can
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References Alharthy A. and Bethe, J
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Fraser, C.S., Baltsavias, E. and Gr
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Khoshelham K., 2004. Building Extra
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Sequeira, V., Ng, K., Wolfart, E.,
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Index of Figures Figure 2-1: Senaat
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Index of Tables Table 2-1: Aerial i
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90 Senaatti by Delft. Wireframe mod
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92 Espoonlahti by IGN, with and wit
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94 Senaatti by IGN, with and withou
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96 Hermanni by Aalborg.
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98 CyberCity ICC laser+aerial ICC l
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100 IGN FOI outlines Nebel+Partner
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Difference Images, Whole Test Site,
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Difference Images, Whole Test Site,
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Difference Images, Modelled Buildin
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Difference Images, Modelled Buildin
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EuroSDR-Project Commission 2 “Ima
- Page 116 and 117: 2 Project highlights The project Ch
- Page 118 and 119: 116 Feature Characteristics Object
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- Page 122 and 123: 120 Figure 4-1: Orthophotomosaic fr
- Page 124 and 125: 4.1.2 Segmentation The first step o
- Page 126 and 127: 124 Class Features Vegetation Ratio
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- Page 130 and 131: Two types of changes were assigned
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- Page 134 and 135: 132 Figure 4-13: Orthophotos of stu
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- Page 138 and 139: 136 Figure 4-16: Change maps of Swi
- Page 140 and 141: spectral reflectance. Shadows (blac
- Page 142 and 143: 140 Figure 4-19: Change map of Germ
- Page 144 and 145: Figure 4-21: Diagnostics as given b
- Page 146 and 147: 144 Automatic Accepted Rejected Hum
- Page 148 and 149: 4.4.2 Segmentation, classification
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- Page 156 and 157: 154 Figure 4-38: Subset of change m
- Page 158 and 159: 5 Conclusions and Outlook In this p
- Page 161: Annex 1 EuroSDR Change Detection Wo
- Page 164 and 165: What is the impact of changing spat
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- Page 171 and 172: Table 3 gives an overview over the
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- Page 188 and 189: 1.2 Contest Phases In phase 1 test
- Page 190 and 191: 188 Figure 1: Data set Copenhagen,
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- Page 194 and 195: 3 Reference Data In order to have a
- Page 196 and 197: a) c) 194 Copenhagen Fjärdhundra T
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- Page 204 and 205: 5.2 Test Site Fjärdhundra 5.2.1 Ar
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- Page 210 and 211: Detection of buildings was not a bi
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- Page 214 and 215: 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?
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C. Customers and applications List
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General information Corresponding P
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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
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45 Murray, K. (ed.): OEEPE Workshop