EuroSDR Projects - Host Ireland

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60 IQR [m] IQR [m] IQR [m] 1.2 1.0 0.8 0.6 0.4 0.2 0.0 1.2 1.0 0.8 0.6 0.4 0.2 0.0 1.2 1.0 0.8 0.6 0.4 0.2 0.0 IGN Espoonlahti, all buildings IQR Height IQR North IQR East CyberCity Hamburg Stuttgart IGN ICC laser+aerial Hermanni IQR Height IQR North IQR East IQR Height IQR North IQR East Nebel+Partner Nebel+Partner ICC laser C+B Technik FOI outlines FOI CyberCity Stuttgart IGN ICC laser+aerial Nebel+Partner ICC laser Delft Aalborg C+B Technik Dresden FOI outlines FOI Amiens FOI outlines FOI IQR [m] IQR [m] 1.2 1.0 0.8 0.6 0.4 0.2 0.0 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Espoonlahti, selected buildings IQR Height IQR North IQR East CyberCity Hamburg Stuttgart IGN ICC laser+aerial IQR Height IQR North IQR East CyberCity Hamburg Stuttgart IGN ICC laser+aerial Senaatti Nebel+Partner ICC laser C+B Technik FOI outlines FOI Nebel+Partner ICC laser Delft C+B Technik Dresden FOI outlines FOI Figure 4-5: Location accuracy (IQR) of the models with respect to single points. Figure 4-5 shows the differences between various models with respect to elevation and planimetric errors for single point targets. The errors are given separately for north-south and east-west directions due to the different point spacing in along and across track directions of TopoSys systems.
CyberCity achieved a good quality in all three Finnish test sites. However, it should be remembered that measurements in CyberCity are done manually from stereoscopic images and automation is mainly used for connecting points into 3D models. Thus, the manual part of the process is verified in this analysis. The quality improved when image quality was better, but in general the accuracy variation within the test sites was low. The Hamburg and Stuttgart models were affected more by the site and site-wise data characteristics. Since the Hermanni test site was relatively easy, the Stuttgart model used high automation in Hermanni resulting in decreased performance. With the most difficult test site, Espoonlahti, the Stuttgart model was almost as good with all buildings as the CyberCity model and even better with selected buildings (same building modelled by all partners). It has to be remembered that the Stuttgart process was done by a student and with CyberCity the process was done by an expert. The Hamburg model showed lower performance than these two other photogrammetric techniques even though it was based on a more standard photogrammetric process but with an inexperienced operator. Hybrid techniques using TerraScan (see ICC laser+aerial) showed a quality almost comparable to the best photogrammetric methods. The same method (i.e. TerraScan) used by Nebel and Partner showed typically lower accuracy, which can be explained by the lower amount of time spent and the lower amount of aerial image information used in the process. In ICC, aerial images and image orientations were used to measure the outlines, and in Nebel and Partner, images were used for visual interpretation purposes. It seems that good quality results can be obtained when building outlines are measured using image information and laser-derived planes. In Hermanni, where all participants using laser data provided a model, the ICC model (based on TerraScan) showed the best accuracy. Also, the Delft and Aalborg models (the latter developed by undergraduate students within the project) resulted in reasonably good accuracy. Delft, Aalborg and C+B Technik mainly used given ground plans to determine building location, so planimetric accuracy is directly affected by the ground plans which do not include roof eaves. This can also be seen in the results: the planimetric accuracy is almost the same for these three and also point deviations in Hermanni (Appendix 2) seem to have “common” points. It should be noted that the generation of the FOI model was fully automatic and their newest extraction algorithm was based on the use of first and last pulse data, and last pulse data was not provided in the test, since it was not available from all test sites. C+B did exceptionally well in Espoonlahti, which was the most difficult area, but they provided only the buildings where outlines were given. One possible explanation for the variability of the C+B performance in various test sites is that since it is based on TINs, it works better than plane-based models (laser points defining a plane) with more complex and with small structures. Also, the Dresden model performed extremely well in Senaatti, but not in Hermanni. The number of points used in the analysis was extremely low for the Dresden model. 61
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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...................
Page 13 and 14: Abstract The objective of the EuroS
Page 15 and 16: The analysis of the structural qual
Page 17 and 18: Figure 2-3: Hermanni test site. Fig
Page 19 and 20: Espoonlahti Hermanni Senaatti Photo
Page 21 and 22: 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: All targets Eaves Ridges Height IGN
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 and 74: In building length determination (F
Page 75 and 76: degrees, std 6.3 degrees). In Senaa
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
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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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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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