EuroSDR Projects - Host Ireland

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- what is the degree of completeness of road extraction (incl. possible errors) and for what land cover, e.g., 0% in forests, 90% in open rural areas, 50% in suburban areas with detached houses, 20% in dense urban areas - what is the percentage of manual pre- and post-editing required, e.g., 70% automatic computer processing, 30% manual editing 21. Independently of whether road extraction is manual, or (semi)automated: - Are the accuracy measures valid global or for individual road segments? - What is the acquisition time incl. both automated and manual processes per square km on a state-of-the-art computer for a region of average road density (e.g., mixed rural and urban)? updating road data: approximately 0.5 hours per square km - What computer operating system is used in the processes involved in road extraction? UNIX Linux Windows 22. What data formats are used for the road data incl. their attributes? Internally: Intergraph; for exchange: NFF, shape, EDBS, DXF 23. What system is used for the storage and management of the road data? Intergraph MG Dynamo Is the system? Relational Object-relational Object-oriented B. How does your organization plan to extract road data in the near/middle future? If you plan any changes for the points listed under A, please explain them here. Information provided by (governmental) road construction agencies; local topographers When do you plan to implement the above changes? E.g., in 1-2 years. at the moment (2002) 254
C. Customers and applications List below all applications for which your organization is using the road data internally, i.e., excluding selling them to third parties. Production of DTK 25 (digital topographic map 1 : 25 000) List below all your current groups of customers and for each list the application and briefly describe the required road data. E.g., list geometric accuracy and attributes needed, how upto-date the data should be, etc. For each required road attribute give its importance from a user/application point of view as low, medium, high. If, e.g., a group of customers are large transportation companies, the application is fleet management and optimization, and the requirements for the road data are: not older than 1 year, planimetric geometric accuracy 5 m RMS, no height information necessary. Attributes needed are road width (high), number of lanes (high), and surface material (medium). Rescue services (fire brigades, police, ambulances) – navigation systems; mapping (city plans); engineering companies (urban planning, etc.) List below all your potential groups of customers (only those that are safely potential customers, avoiding speculations) and for each customer, list the application and briefly describe the required road data. E.g., list geometric accuracy and attributes needed, how upto-date the data should be, etc. For each needed road attribute give its importance from a user/application point of view as low, medium, high. location based services (insurance companies, parcel service) geo marketing D. Requirements of your organization on the data Here, the desired requirements of your organizations should be listed if they differ from the current status listed under A. For each required road attribute give its importance from a user/application point of view as low, medium, high. Please, add in the field below any additional remarks Thank you for your collaboration 255
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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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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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Page 207 and 208: 5.3 Test Site Oberpfaffenhofen 5.3.
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Page 211 and 212: cies in analysis. The following fig
Page 213 and 214: 5.4.2 Qualitative Comparison The ra
Page 215 and 216: Acknowledgments We thank all partic
Page 217: Index of Tables Table 1: Contest Ph
Page 221 and 222: Abstract Roads are important object
Page 223 and 224: overview over the whole area. We ra
Page 225 and 226: m and above no longer plays an impo
Page 227 and 228: 3 Test Data and Evaluation Criteria
Page 229 and 230: and straight enough. • Markus Ger
Page 231 and 232: Name (best) Test area Completeness
Page 233 and 234: • ADS40_1 and 2: Results for both
Page 235 and 236: • Ikonos1_Sub1: This image shows
Page 237 and 238: Figure 7: Ikonos3_Sub1 - Bacher; co
Page 239 and 240: Finally, we want to note some impor
Page 241 and 242: load nor response time for anybody
Page 243: Zhang, C., 2004. Towards an Operati
Page 246 and 247: objects, and their cooperation with
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Page 250 and 251: Possibly only parts of images will
Page 252 and 253: How many people are employed in you
Page 254 and 255: 7. What is the average time differe
Page 259 and 260: Appendix 3: Questionnaire for Resea
Page 261 and 262: If others please specify: 2. What d
Page 263 and 264: Complex crossings (more than 4 road
Page 265 and 266: Combination of a) or b) with c) [0]
Page 267 and 268: If yes, using: DTM and / or [2] D
Page 269 and 270: Appendix 4: General Characteristics
Page 271: Most important features for practic
Page 274 and 275: IKONOS The images come from the SI
Page 277: Appendix 6: Documentation by Beumie
Page 281 and 282: Appendix 8: Documentation by Zhang
Page 283 and 284: LIST OF OEEPE/EuroSDR OFFICIAL PUBL
Page 285 and 286: 25 Ducher, G.: Test on Orthophoto a
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