200 160,0 140,0 120,0 100,0 80,0 60,0 40,0 20,0 0,0 Forest Area - SAR incorrect pos. 10,5 9,0 15,8 50,6 25,5 7,2 incorrect neg. 16,2 20,7 19,8 5,2 16,7 32,8 correct 83,8 79,3 80,2 94,8 83,3 67,2 140,0 120,0 100,0 80,0 60,0 40,0 20,0 0,0 Int 1 Int 2 Int 3 Int 4 Int 5 Int 6 Lakes - SAR incorrect pos. 20,5 30,7 20,8 10,7 5,8 2,1 incorrect neg. 46,2 49,1 50,1 46,2 72,5 85,1 correct 53,8 50,9 49,9 53,8 27,5 14,9 120,0 100,0 80,0 60,0 40,0 20,0 0,0 160,0 140,0 120,0 100,0 Figure 15: Copenhagen Result, Forested Area Int 1 Int 2 Int 3 Int 4 Int 5 Int 6 Railw ays & Roads - SAR 80,0 60,0 40,0 20,0 0,0 Forest Areas - optical Int 2 Int 3 Int 7 Int 8 incorrect pos. 11,6 7,0 25,1 7,7 incorrect neg. 10 , 6 14 , 0 5, 2 17, 3 correct 89,4 86,0 94,8 82,7 140,0 120,0 100,0 80,0 60,0 40,0 20,0 Figure 16: Copenhagen Result, Lakes Int 1 Int 2 Int 3 Int 4 Int 5 Int 6 Int 9 incorr. positive 5,4 10,6 9,2 14,4 8,3 5,8 0,0 incorr. negative 23,6 20,5 23,0 16,8 28,2 35,5 51,7 incorr. position 6,0 7,0 6,0 6,7 8,0 9,3 3,7 correct 70,4 72,5 71,0 76,6 63,8 55,2 44,6 0,0 Lakes - optical Int 2 Int 3 Int 7 Int 8 incorrect pos. 12,9 11,1 13,2 7,6 incorrect neg. 35,3 40,1 31,2 42,9 correct 64,7 59,9 68,8 57,1 Railways & Roads - optical 120,0 100,0 80,0 60,0 40,0 20,0 0,0 Int 2 Int 3 Int 7 Int 8 incorr. positive 2,7 12,7 0,7 incorr. negative 9,6 14,6 19,1 incorr. position 4,2 3,9 3,6 correct 86,2 81,4 77,3 Figure 17: Copenhagen Result, Linear Objects
5.1.2 Qualitative Comparison In addition to the numerical analysis, a qualitative comparison of special objects was performed, because some participants classified more and detailed objects than others. The results of qualitative comparison are shown in Table 6. SAR image Optical image Freeway detected detected Main Road predominantly detected detected Alleys few to none predominantly detected Trenches none few to none Lakes big = good, small = partially big = all, small = predominantly Trees depends on interpreter, but possible separate ones mostly detected Buildings big ones detected, smaller ones partially but not always correct big and small ones detected exemplary Golf Course only one detection only two detections Sports Field partially detected nearly all of them 5.1.3 Summary – Copenhagen land use boundary, ruins, barracks, parking lots Table 6: Qualitative Comparison of Copenhagen The results of visual interpretation of Copenhagen show that big and continuous areas like forest, agricultural and built-up areas were detected equally well in SAR and in optical imagery. Smaller areal objects like lakes and separate buildings are more difficult to extract from SAR imagery. Concerning linear objects it can be ascertained that highways and main roads were detected equally well in both types of image. Smaller linear objects, such as alleys, trenches and secondary roads, cannot be detected in SAR but partially in optical imagery. In addition, classification of roads versus alleys appeared to be easier in optical images. The golf course, which is placed on the left side of the images, was mostly detected as green corridor. Only two interpreters of optical and one interpreter of SAR imagery classified that area as a golf course. The classification of this area in the ground truth (reference) data could only be done well with information from the topographic map. The visual analysis shows that some interpreters used a more detailed classification than others. They additionally detected land use boundaries, ruins, barracks and parking lots. These objects cannot be approved with certainty, because there is no evidence for them in the available ground truth data. 201
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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
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2 Project highlights The project Ch
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116 Feature Characteristics Object
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New are those combinations that ind
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120 Figure 4-1: Orthophotomosaic fr
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4.1.2 Segmentation The first step o
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124 Class Features Vegetation Ratio
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4.1.4 Change map As the results of
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Two types of changes were assigned
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130 Figure 4-11: Evaluation of chan
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132 Figure 4-13: Orthophotos of stu
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different villages were identified
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136 Figure 4-16: Change maps of Swi
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spectral reflectance. Shadows (blac
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140 Figure 4-19: Change map of Germ
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Figure 4-21: Diagnostics as given b
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144 Automatic Accepted Rejected Hum
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4.4.2 Segmentation, classification
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differentiated very well. The chang
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How many people are employed in you
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7. What is the average time differe
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Appendix 3: Questionnaire for Resea
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If others please specify: 2. What d
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Complex crossings (more than 4 road
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Combination of a) or b) with c) [0]
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If yes, using: DTM and / or [2] D
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Appendix 4: General Characteristics
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Most important features for practic
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IKONOS The images come from the SI
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Appendix 6: Documentation by Beumie
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Appendix 8: Documentation by Zhang
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LIST OF OEEPE/EuroSDR OFFICIAL PUBL
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25 Ducher, G.: Test on Orthophoto a