EuroSDR Projects - Host Ireland

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[degrees] 72 [degrees] 6.0 5.0 4.0 3.0 2.0 1.0 0.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 All observations IGN Nebel+ Partner FOI outlines FOI N 5 1 0 2 Min -34.97 -1.19 -4.50 Max 15.93 -1.19 11.90 Median -0.49 -1.19 3.70 Mean -4.80 -1.19 3.70 STD 19.32 11.60 RMSE 17.93 1.19 9.00 IQR 40.53 16.40 R 2 0.1306 1.0000 IGN Table 4-22: Amiens roof inclination accuracy in degrees. Roof inclination, Hermanni IQR RMSE CyberCity Stuttgart IGN ICC laser+aerial Nebel+Partner ICC laser Delft Aalborg C+B Technik Dresden FOI outlines FOI Roof inclination, Amiens Nebel+Partner IQR RMSE FOI outlines FOI [degrees] 18.0 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0 Roof inclination, Senaatti IQR RMSE CyberCity Hamburg Stuttgart IGN ICC laser+aerial Nebel+Partner ICC laser Delft C+B Technik Dresden FOI outlines FOI Figure 4-15: Roof inclination accuracy of the models. IQR: all observations, RMSE: outliers removed. Figure 4-15 gives the roof inclination accuracy for each model, defined with IQR and RMSE. Roof inclination determination was more accurate when using laser data than photogrammetry, but a large variation in quality exists due to methods and test sites (i.e. complex buildings). The RMSE (all observations included) using laser scanning for roof inclination varied from 0.3 to 15.9 degrees (mean 2.7 degrees, median 0.85 degrees, std 4.4 degrees). The corresponding values for aerial image assisted laser scanning ranged from 0.6 and 2.3 degrees (mean 1.3 degrees, median 1.1 degrees, std 0.6 degrees) and for photogrammetry ranged from 1.0 to 17.9 degrees (mean 5.2 degrees, median 3.2
degrees, std 6.3 degrees). In Senaatti and Amiens, the roof inclinations are steep and roofs short, so even small errors in target height determination lead to large errors in inclination angle. The Hermanni test site is relatively easy for both methods. In Hermanni the accuracy of roof inclination determination was about 2.5 degrees for photogrammetric methods and about 1 degree (RMSE, all observations included) for laser based methods. In Hermanni the best result was obtained with the Dresden model and basically all laser based methods resulted in less than 1 degree error (RMSE). The C+B approach had a larger error than the photogrammetric processes. Most probably the TIN based principle of C+B does not give as good results as using all the points hitting one surface and defining a plane using all of them. In photogrammetry, the roof inclination is obtained from two measurements and therefore, the same accuracy as laser was clearly not achieved. When the building size was smaller and/or the pulse density was lower, this difference between photogrammetry and laser scanning was reduced or even disappeared (Figure 4-15). 4.3 Total Relative Building Area and Shape Dissimilarity Buildings or parts of buildings are missing in many models. The reasons for this are not always known, but the shadowing of big trees and the complexity of buildings can certainly cause problems. Participants may also strive for different goals in building extraction; some want to model the buildings in as detailed a manner as possible and some are aiming for basic building form only. For automatic methods vegetation also causes problems as trees may be classified as buildings. With other methods the extra buildings are mainly temporary constructions, such as site huts for construction sites, which are usually impossible to separate out from “real” buildings. Total relative building areas and shape dissimilarities for the whole test site and for modelled buildings at Espoonlahti are shown in Table 4-23 and Figure 4-16 and at Hermanni in Table 4-24 and Figure 4-17. Images showing the differences in 2D between reference raster and delivered models are in Appendix 3. Cyber City Hamburg Stuttgart IGN ICC laser+ aerial Nebel+ Partner ICC laser C+B Technik Total relative All 98.6 94.2 49.6 97.3 92.3 99.7 89.2 95.2 105.2 108.2 97.6 building area Modelled 100.0 96.8 80.7 97.8 94.8 101.8 94.0 97.4 105.2 111.5 101.4 Total relative shape dissimilarity All 7.7 12.4 52.6 10.7 10.2 11.8 17.5 6.9 10.9 29.9 25.8 Modelled 5.5 10.0 22.8 10.2 7.8 10.1 12.9 4.7 10.9 26.6 22.7 Table 4-23: Total relative building area and shape dissimilarity at Espoonlahti in %. FOI outl FOI FOI 2005 73
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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
Page 23 and 24: Used data Time use Laser Aerial Gro
Page 25 and 26: Step 3: Import to CCModeler Figure
Page 27 and 28: Figure 3-4: Sequence of manual phot
Page 29 and 30: Figure 3-8: Workflow of laser scann
Page 31 and 32: Figure 3-11: Parametric building mo
Page 33 and 34: Each group of connected pixels clas
Page 35 and 36: Figure 3-18: Topological points and
Page 37 and 38: Detailed Description (Letters refer
Page 39 and 40: assumption is made: the two longest
Page 41 and 42: Figure 3-25: A 3D building model wi
Page 43 and 44: ICC used the same methods as Nebel
Page 45 and 46: where p75th is the value at the 75
Page 47 and 48: CyberCity Stuttgart Hamburg IGN ICC
Page 49 and 50: CyberCity Hamburg Stuttgart IGN ICC
Page 51 and 52: Height Cyber- City Hamburg Stuttgar
Page 53 and 54: Height Cyber- City Stuttgart IGN IC
Page 55 and 56: Height Cyber- City Stuttgart IGN IC
Page 57 and 58: Height Cyber- City HamburgStuttgart
Page 59 and 60: Height Cyber- City HamburgStuttgart
Page 61 and 62: All targets Eaves Ridges Height IGN
Page 63 and 64: CyberCity achieved a good quality i
Page 65 and 66: Point density, shadowing of trees a
Page 67 and 68: Height accuracy IQR [m] 1.6 1.4 1.2
Page 69 and 70: All Cyber- Ham- ICC laser+ Nebel+ I
Page 71 and 72: All Cyber- HamStutt- ICC laser+ Neb
Page 73: In building length determination (F
Page 77 and 78: 5 Discussion and Conclusions It can
Page 79 and 80: References Alharthy A. and Bethe, J
Page 81 and 82: Fraser, C.S., Baltsavias, E. and Gr
Page 83 and 84: Khoshelham K., 2004. Building Extra
Page 85 and 86: Sequeira, V., Ng, K., Wolfart, E.,
Page 87 and 88: Index of Figures Figure 2-1: Senaat
Page 89: Index of Tables Table 2-1: Aerial i
Page 92 and 93: 90 Senaatti by Delft. Wireframe mod
Page 94 and 95: 92 Espoonlahti by IGN, with and wit
Page 96 and 97: 94 Senaatti by IGN, with and withou
Page 98 and 99: 96 Hermanni by Aalborg.
Page 100 and 101: 98 CyberCity ICC laser+aerial ICC l
Page 102 and 103: 100 IGN FOI outlines Nebel+Partner
Page 105 and 106: Difference Images, Whole Test Site,
Page 107 and 108: Difference Images, Whole Test Site,
Page 109 and 110: Difference Images, Modelled Buildin
Page 111 and 112: Difference Images, Modelled Buildin
Page 113: EuroSDR-Project Commission 2 “Ima
Page 116 and 117: 2 Project highlights The project Ch
Page 118 and 119: 116 Feature Characteristics Object
Page 120 and 121: New are those combinations that ind
Page 122 and 123: 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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150 Figure 4-31: Ultracam real-colo
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152 Figure 4-35: Classification of
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154 Figure 4-38: Subset of change m
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5 Conclusions and Outlook In this p
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Annex 1 EuroSDR Change Detection Wo
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What is the impact of changing spat
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Annex 2 Paper presented at IGARSS05
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covering 2 x 2 km with reference da
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170 Red roof Bright object 0 20 40
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Annex 3 EuroSDR CD Workshop Nicosia
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176 method 1 indicates land cover c
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Annex 4 Comments on change map by B
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enzen mehr zu erwarten. Doch laut
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Abstract The EuroSDR “Sensor and
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2 Test Sites and Test Data The test
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Figure 3: Data set Fjärdhundra, ag
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The map of Sweden is only available
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3.2 Accounting for Different Acquis
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4 Analysis 4.1 Basic Information In
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4.2.2 Linear Objects Since it is no
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5 Results of Phase I 5.1 Test Site
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5.1.2 Qualitative Comparison In add
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140,0 120,0 100,0 80,0 60,0 40,0 20
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5.3 Test Site Oberpfaffenhofen 5.3.
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140,0 120,0 100,0 80,0 60,0 40,0 20
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cies in analysis. The following fig
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5.4.2 Qualitative Comparison The ra
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Acknowledgments We thank all partic
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Index of Tables Table 1: Contest Ph
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Abstract Roads are important object
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overview over the whole area. We ra
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m and above no longer plays an impo
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3 Test Data and Evaluation Criteria
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and straight enough. • Markus Ger
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Name (best) Test area Completeness
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• ADS40_1 and 2: Results for both
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• Ikonos1_Sub1: This image shows
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Figure 7: Ikonos3_Sub1 - Bacher; co
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Finally, we want to note some impor
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load nor response time for anybody
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Zhang, C., 2004. Towards an Operati
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objects, and their cooperation with
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Belgium, provided by their NMCAs. H
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Possibly only parts of images will
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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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- what is the degree of completenes
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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
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