EuroSDR Projects - Host Ireland

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spectral reflectance. Shadows (black) are visible in some parts of the image, especially close to large building blocks and forest edges. The class other urban object (yellow) is here more related to bare fields and, as it is not clearly defined, will be treated as such in the evaluation. 138 Figure 4-18: Classification of German data set For the comparison of the classification with the reference data a new evaluation matrix was set up to account for the different classes of the ATKIS data base (see Table 4-9). Each class combination is assigned to one of the four plausibility classes. As described above the combination of other urban object area in the classification and farmland in the land use data is considered to be identical. The result of the evaluation is shown in Figure 4-19. Very large areas are assigned to questionable. In most cases these are areas which are considered to be grassland in the ATKIS data base but are actually covered by forest in the classification. Some areas where building activities had taken place could be identified and are shown in red. Vegetation in urban areas is highlighted in yellow as are roads along fields. Although most of these roads show up in the initial classification, they were later merged with fields in the refinement of the classification. If these roads were to represent a problem when using the change map, they could be removed by including the first classification stage in the evaluation.
Land use Classification Forest Meadow Field Red roof Bright object Grey object Other urban object Allotment Built up - special feature Cemetery Farmland Grassland Grove Industrial areas Market garden Mix. of res./ind. areas Nondescript Purification plant Recreational area Residential area Specialised cultivation Sports facilities Sports field Road identical plausible questionable new Table 4-9: Evaluation matrix for data from Germany 139
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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
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
Page 124 and 125: 4.1.2 Segmentation The first step o
Page 126 and 127: 124 Class Features Vegetation Ratio
Page 128 and 129: 4.1.4 Change map As the results of
Page 130 and 131: Two types of changes were assigned
Page 132 and 133: 130 Figure 4-11: Evaluation of chan
Page 134 and 135: 132 Figure 4-13: Orthophotos of stu
Page 136 and 137: different villages were identified
Page 138 and 139: 136 Figure 4-16: Change maps of Swi
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
Page 150 and 151: differentiated very well. The chang
Page 152 and 153: 150 Figure 4-31: Ultracam real-colo
Page 154 and 155: 152 Figure 4-35: Classification of
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
Page 167: Annex 2 Paper presented at IGARSS05
Page 170 and 171: covering 2 x 2 km with reference da
Page 172 and 173: 170 Red roof Bright object 0 20 40
Page 175: Annex 3 EuroSDR CD Workshop Nicosia
Page 178 and 179: 176 method 1 indicates land cover c
Page 181: Annex 4 Comments on change map by B
Page 184 and 185: enzen mehr zu erwarten. Doch laut
Page 187 and 188: Abstract The EuroSDR “Sensor and
Page 189 and 190: 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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