Collective sensing: Integrating geospatial technologies to understand urban systems-An overview

Remote Sensing

Volumn 3, Issue 8, 2011, Pages 1743-1776

  Blaschke, Thomas ^a,b   Hay, Geoffrey J ^c   Weng, Qihao ^d   Resch, Bernd ^b  

^a UNIVERSITY OF SALZBURG   (Austria)

^b Research Studios Austria   (Austria)

^c UNIVERSITY OF CALGARY   (Canada)

^d INDIANA STATE UNIVERSITY   (United States)

Author keywords

Collective sensing; Future trends; Human environment interactions; In situ sensing; Sensor web; Smart city; Urban remote sensing

Indexed keywords

COLLECTIVE SENSING; FUTURE TRENDS; HUMAN-ENVIRONMENT INTERACTIONS; IN SITU SENSING; SENSOR WEB; SMART CITY; URBAN REMOTE SENSING;

GEOGRAPHIC INFORMATION SYSTEMS; REMOTE SENSING; USER INTERFACES; WORLD WIDE WEB;

TECHNOLOGY;

EID: 80052644576     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs3081743     Document Type: Review

References (148)

1. Crutzen, P.J. Geology of mankind. Nature 2002, 415, 23.
2. World Population Data Sheet 2009; PRB (Population Reference Bureau): Washington, DC, USA, 2009.
3. United Nations Department of Economic and Social Affairs. Population Division; No.2; UN: New York, NY, USA, 2010.
4. Energy Mapping Study; Canadian Urban Institute for the City of Calgary: Toronto, ON, Canada, 2008. Available online: http://www.calgary.ca/docgallery/BU/planning/pdf/plan_it/energy_mapping_study.pdf (accessed on 13 March 2011).
5. Bailie, A.; Beckstead, C. Canada's Coolest Cities; Sustainable Energy Solutions Technical Report; The Pembina Institute: Drayton Valley, AB, Canda, 2010. Available online: http://pubs.pembina.org/reports/coolest-cities-technical-report.pdf (accessed on 13 March 2011).
6. Zardini, M. Sense of the City: An Alternate Approach to Urbanism; Lars Müller Publishers: Baden, Switzerland, 2006.
7. Hall, P. The World Cities, 3rd ed.; Weidenfeld and Nicolson: London, UK, 1966.
8. Hall, P.; Pfeiffer, U. URBAN 21-Expertenbericht Zur Zukunft der Städte; Deutsche Verlagsanstalt: Stuttgart, Germany, 2000.
9. Castells, M. The rise of network society. In The Information Age: Economy, Society and Culture; Castells, M., Ed.; Blackwell: Oxford, UK, 1996; Volume 1.
10. Friedmann, J. The world city hypothesis. Dev. Change 1986, 17, 69-83.
11. Droege, P. The Renewable City: A Comprehensive Guide to an Urban Revolution; John Wiley & Sons, Ltd.: Chichester, UK, 2006.
12. Mitchell, W.J. City of Bits: Space, Place, and the Infobahn; MIT Press: Cambridge, MA, USA, 1996.
13. Marceau, D.; Benenison, I. Advanced Geospatial Simulation Models; Bentham Science Publishers: Hilversum, The Netherlands, 2011; in press.
14. Jensen, J.R. Remote Sensing of the Environment: An Earth Resource Perspective, 2nd ed.; Pearson Prentice Hall: Upper Saddle River, NJ, USA, 2007.
15. ITRES. CASI-1500 Hyperspectral Imager; 2011. Available online: http://www.itres.com/products/imagers/casi1500 (accessed on 13 March 2011).
16. Thomas, N.; Hendrix, C.; Congalton, R.G. A comparison of urban mapping methods using high-resolution digital imagery. Photogramm. Eng. Remote Sensing 2003, 69, 963-972.
17. Weng, Q.; Quattrochi, D.A. Urban Remote Sensing; CRC Press/Taylor and Francis: Boca Raton, FL, USA, 2006.
18. Ehlers, M. New developments and trends for urban remote sensing. In Urban Remote Sensing; Weng, Q., Quattrochi, D.A., Eds.; CRC Press: Boca Raton, FL, USA, 2006; pp. 357-375.
19. Herold, M.; Rogers, D.A. Remote sensing of urban and suburban areas. In Remote Sensing of Urban and Suburban Areas; Rashed, T., Jürgens, C., Eds.; Springer: Berlin, Germany, 2010; pp. 47-65.
20. Rashed, T.; Jürgens, C. Remote Sensing of Urban and Suburban Areas; Springer: Berlin, Germany, 2010.
21. Lu, D.; Mausel, P.; Brondízio, E.; Moran, E. Change detection techniques. Int. J. Remote Sens. 2004, 25, 2365-2407.
22. Jensen, J.R. Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd ed.; Pearson Prentice Hall: Upper Saddle River, NJ, USA, 2005.
23. Lu, D.; Weng, Q. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 2007, 28, 823-870.
24. Weng, Q. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS J. Photogramm. 2009, 64, 335-344.
25. Weng, Q. Advances in Environmental Remote Sensing: Sensors, Algorithms and Applications CRC Press/Taylor and Francis: Boca Raton, FL, USA, 2011.
26. Maktav, D.; Erbek, F.S.; Jürgens, C. Remote sensing of urban areas. Int. J. Appl. Earth Obs. Geoinf. 2005, 26, 655-659.
27. Andersson, E. Urban landscapes and sustainable cities. Ecol. Soc. 2006, 11, 34.
28. Hardin, P.J.; Jackson, M.W.; Otterstrom, S.M. Mapping, measuring, and modeling urban growth. In Geo-Spatial Technologies in Urban Environments: Policy, Practice, and Pixels, 2nd ed.; Jensen, R.R., Gatrell, J.D., McLean, D.D., Eds.; Springer-Verlag: Berlin, Germany, 2007; pp. 141-176.
29. Longley, P.A. Geographical Information Systems: Will developments in urban remote sensing and GIS lead to 'better' urban geography? Progr. Human Geogr. 2002, 26, 231-239.
30. Lu, D.; Weng, Q. Extraction of urban impervious surfaces from IKONOS imagery. Int. J. Remote Sens. 2009, 30, 1297-1311.
31. Hu, X.; Weng, Q. Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method. Geocarto Int. 2011, 26, 3-20.
32. Bhaskaran, S.; Paramananda, S.; Ramnarayan, M. Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data. Appl. Geogr. 2010, 30, 650-665.
33. Myint, S.W.; Gober, P.; Brazel, A.; Grossman-Clarke, S, Weng, Q. Per-pixel versus object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ. 2011, 115, 1145-1161.
34. Roessner, S.; Segl, K.; Heiden, U.; Kaufmann, H. Automated differentiation of urban surfaces based on airborne hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 2001, 39, 1525-1532.
35. Gamba, P.; Dell'Acqua, F. Spectral resolution in the context of very high resolution urban remote sensing. In Urban Remote Sensing; Weng, Q., Quattrochi, D.A., Eds.; CRC Press: Boca Raton, FL, USA, 2006; pp. 377-391.
36. Yu, Q.; Gong, P.; Clinton, N.; Biging, G.; Kelly, M.; Schirokauer, D. Object-based detailed vegetation classification with airborne high resolution remote sensing imagery. Photogramm. Eng. Remote Sensing 2006, 72, 799-781.
37. Herold, M.; Liu, X.H.; Clarke, K.C. Spatial metrics and image texture for mapping urban land-use. Photogramm. Eng. Remote Sensing 2003, 69, 991-1001.
38. Jensen, J.R.; Cowen, D.C. Remote sensing of urban/suburban infrastructure and socioeconomic attributes. Photogramm. Eng. Remote Sensing 1999, 65, 611-622.
39. Hinks, T.; Carr, H.; Laefer, D.F. Flight optimization algorithms for aerial LiDAR capture for urban infrastructure model generation. J. Comput. Civil Eng. 2009, 23, 330-339.
40. Goodwin, N.R.; Coops, N.C.; Tooke, T.R.; Christen, A.; Voogt, J.A. Characterizing urban surface cover and structure with airborne LiDAR technology. Can. J. Remote Sens. 2009, 35, 297-309.
41. Weng, Q.; Lu, D. Landscape as a continuum: An examination of the urban landscape structures and dynamics of Indianapolis city 1991-2000. Int. J. Remote Sens. 2009, 30, 2547-2577.
42. Mather, P.M. Land cover classification revisited. In Advances in Remote Sensing and GIS; Atkinson, P.M., Tate, N.J., Eds.; Wiley: New York, NY, USA, 1999; pp. 7-16.
43. Hay, G.J.; Niemann, K.O.; McLean, G. An object-specific image-texture analysis of H-resolution forest imagery. Remote Sens. Environ. 1996, 55, 108-122.
44. Wang, F. Fuzzy supervised classification of remote sensing images. IEEE Trans. Geosci. Remote Sens. 1990, 28, 194-201.
45. Asner, G.P.; Hicke, J.A.; Lobell, D.B. Per-pixel analysis of forest structure. Vegetation indices, spectral mixture analysis and canopy reflectance modeling. In Remote Sensing of Forest Environments. Concepts and Case Studies; Wulder, M.A., Franklin, S.E., Eds.; Kluwer Academic Publishers: Dordrecht, The Netherlands, 2003; pp. 209-254.
46. Blaschke, T.; Lang, S.; Hay, G. Object Based Image Analysis; Springer: Berlin, Germany, 2008.
47. Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogramm. 2010, 65, 2-16.
48. Lang, S.; Blaschke, T. Bridging remote sensing and GIS-What are the main supporting pillars? In International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences; ISPRS: Vienna, Austria, 2006; Vol. XXXVI-4/C42.
49. Walker, J.; Blaschke, T. Object-based landcover classification for the Phoenix metropolitan area: Optimization vs. transportability. Int. J. Remote Sens. 2008, 29, 2021-2040.
50. Hay, G.J.; Castilla, G. Geographic object-based image analysis (GEOBIA): A new name for a new discipline? In Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications; Blaschke, T., Lang, S., Hay, G.J., Eds.; Springer: Berlin, Germany, 2008; pp. 75-89.
51. Castilla, G.; Hay, G.J. Image-objects and geo-objects. In Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications; Blaschke, T., Lang, S., Hay, G.J., Eds.; Springer: Berlin, Germany, 2008; pp. 92-110.
52. Liu, D.; Xia, F. Assessing object-based classification: Advantages and limitations. Remote Sens. Lett. 2010, 1, 187-194.
53. Jin, M.S.; Kessomkiat, W.; Pereira, G. Satellite-observed urbanization characters in Shanghai, China: Aerosols, Urban Heat Island effect, and land-atmosphere interactions. Remote Sens. 2011, 3, 83-99.
54. Aubrecht, C.; Steinnocher, K.; Hollaus, M.; Wagner, W. Integrating earth observation and GIScience for high resolution spatial and functional modeling of urban land use. Comput. Environ. Urban Syst. 2009, 33, 15-25.
55. Zhou, W.; Troy, A.; Grove, M. Object-based land cover classification and change analysis in the baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors 2008, 8, 1613-1636.
56. Kux, H.J.; Araujo, E.H.G. Object-based image analysis using QuickBird satellite images and GIS data, case study Belo Horizonte (Brazil). In Object Based Image Analysis; Blaschke, T., Lang, S., Hay, G.J., Eds.; Springer: Berlin, Germay, 2008; pp. 571-588.
57. Kressler, F.; Steinnocher, K. Object-oriented analysis of image and LiDAR data and its potential for dasymetric mapping applications. In Object Based Image Analysis; Blaschke, T., Lang, S., Hay, G.J., Eds.; Springer: Berlin, Germay, 2008; pp. 611-624.
58. Ehlers, M.; Jadkowski, M.A.; Howard, R.R.; Brostuen, D.E. Application of SPOT data for regional growth analysis and local planning. Photogramm. Eng. Remote Sensing 1990, 56, 175-180.
59. Treitz, P.M.; Howard, P.J.; Gong, P. Application of satellite and GIS technologies for land-cover and land-use mapping at the rural-urban fringe: A case study. Photogramm. Eng. Remote Sensing 1992, 58, 439-448.
60. Harris, P.M.; Ventura, S.J. The integration of geographic data with remotely sensed imagery to improve classification in an urban area. Photogramm. Eng. Remote Sensing 1995, 61, 993-998.
61. Weng, Q. Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS, and stochastic modeling. J. Environ. Manage. 2002, 64, 273-284.
62. Wilkinson, G.G. A review of current issues in the integration of GIS and remote sensing data. Int. J. Geogr. Inf. Syst. 1996, 10, 85-101.
63. Harvey, W.; McGlone, J.C., McKeown, D.M.; Irvine, J.M. User-centric evaluation of semi-automated road network extraction. Photogramm. Eng. Remote Sensing 2004, 70, 1353-1364.
64. Song, M.; Civco, D. Road extraction using SVM and image segmentation. Photogramm. Eng. Remote Sensing 2004, 70, 1365-1372.
65. Doucette, P.; Agouris, P.; Stefanidis, A. Automated road extraction from high resolution multispectral imagery. Photogramm. Eng. Remote Sensing 2004, 70, 1405-1416.
66. Kim, T.; Park, S.; Kim, M.; Jeong, S.; Kim, K. Tracking road centerlines from high resolution remote sensing images by least squares correlation matching. Photogramm. Eng. Remote Sensing 2004, 70, 1417-1422.
67. Mayer, H. Automatic object extraction from aerial imagery-A survey focusing on building. Comput. Vis. Image Understand. 1999, 74, 138-139.
68. Lee, D.S.; Shan, J.; Bethel, J.S. Class-guided building extraction from IKONOS imagery, Photogramm. Eng. Remote Sensing 2003, 69, 143-150.
69. Lee, D.H.; Lee, K.M.; Lee, S.U. Fusion of lidar and imagery for reliable building extraction. Photogramm. Eng. Remote Sensing 2008, 74, 215-225.
70. Miliaresis, G.; Kokkas, N. Segmentation and object-based classification for the extraction of the building class from LiDAR DEMs. Comput. Geosci. 2007, 33, 1076-1087.
71. Haack, B.; Bryant, N.; Adams, S. Assessment of Landsat MSS and TM data for urban and near-urban land cover digital classification. Remote Sens. Environ. 1987, 21, 201-213.
72. Lu, D.; Weng, Q. Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery. Photogramm. Eng. Remote Sensing 2004, 70, 1053-1062.
73. Weng, Q.; Hu, X. Medium spatial resolution satellite imagery for estimating and mapping urban impervious surfaces using LSMA and ANN. IEEE Trans. Geosci. Remote Sens. 2008, 46, 2397-2406.
74. Yeh, A.G.O.; Li, X. An integrated remote sensing and GIS approach in the monitoring and evaluation of rapid urban growth for sustainable development in the Pearl River Delta, China. Int. Plan. Studies 1997, 2, 193-210.
75. Cheng, J.; Masser, I. Urban growth pattern modeling: A case study of Wuhan City, PR, China. Landscape Urban Plan. 2003, 62, 199-217.
76. Mesev, V. The use of census data in urban image classification. Photogramm. Eng. Remote Sensing 1998, 64, 431-438.
77. Weng, Q. Remote Sensing and GIS Integration: Theories, Methods, and Applications; McGraw-Hill: New York, NY, USA, 2009.
78. Langford, M.; Maguire, D.J.; Unwin, D.J. The areal interpolation problem: Estimating population using remote sensing in a GIS framework. In Handling Geographical Information: Methodology and Potential Applications; Masser, I., Blakemore, M., Eds.; John Wiley & Sons, Inc.: New York, NY, USA, 1991.
79. Lo, C.P. Automated population and dwelling unit estimation from high resolution satellite images: A GIS approach. Int. J. Remote Sens. 1995, 16, 17-34.
80. Sutton, P. Modeling population density with nighttime satellite imagery and GIS. Comput. Environ. Urban Syst. 1997, 21, 227-244.
81. Yuan, Y.; Smith, R.M.; Limp, W.F. Remodeling census population with spatial information from Landsat imagery. Comput. Environ. Urban Syst. 1997, 21, 245-258.
82. Harris, R.J.; Longley, P.A. New data and approaches for urban analysis: Modeling residential densities. Trans. GIS 2000, 4, 217-234.
83. Martin, D.; Tate, N.J.; Langford, M. Refining population surface models: Experiments with Northern Ireland census data. Trans. GIS 2000, 4, 343-360.
84. Harvey, J. Small area population estimation using satellite imagery. Trans. GIS 2000, 4, 611-633.
85. Harvey, J.T. Estimation census district population from satellite imagery: Some approaches and limitations. Int. J. Remote Sens. 2002, 23, 2071-2095.
86. Qiu, F.; Woller, K.L.; Briggs, R. Modeling urban population growth from remotely sensed imagery and TIGER GIS road data. Photogramm. Eng. Remote Sensing 2003, 69, 1031-1042.
87. Li, G.; Weng, Q. Using Landsat ETM+ imagery to measure population density in Indianapolis, Indiana, USA. Photogramm. Eng. Remote Sensing 2005, 71, 947-958.
88. Li, G.; Weng, Q. Fine-scale population estimation: How Landsat ETM+ imagery can improve population distribution mapping? Can. J. Remote Sens. 2010, 36, 155-165.
89. Thomson, C.N. Remote sensing/GIS integration to identify potential low-income housing sites. Cities 2000, 17, 97-109.
90. Hall, G.B.; Malcolm, N.W.; Piwowar, J.M. Integration of remote sensing and GIS to detect pockets of urban poverty: The case of Rosario, Argentina. Trans. GIS 2001, 5, 235-253.
91. Weber, C.; Hirsch, J. Some urban measurements from SPOT data: Urban life quality indices. Int. J. Remote Sens. 1992, 13, 3251-3261.
92. Lo, C.P.; Faber, B.J. Integration of landsat thematic mapper and census data for quality of life assessment. Remote Sens. Environ. 1997, 62, 143-157.
93. Li, G.; Weng, Q. Measuring the quality of life in city of Indianapolis by integration of remote sensing and census data. Int. J. Remote Sens. 2007, 28, 249-267.
94. Liang, B.; Weng, Q. Assessing urban environmental quality change of Indianapolis, United States, by the remote sensing and GIS integration. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2011, 4, 43-55
95. Möller, M.; Blaschke, T. GIS-gestützte Bildanalyse der städtischen vegetation als Indikator urbaner Lebensqualität. Photogramm. Fernerkundung Geoinformation 2006, 10, 19-30.
96. Hinz, S.; Lenhart, D; Leitloff, J. Traffic extraction and characterisation from optical remote sensing data. Photogramm. Rec. 2008, 23, 424-440.
97. Heiple, S.; Sailor, D.J. Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profiles. Energy Build. 2008, 40, 1426-1436.
98. Roth, M.; Oke, T.R.; Emery, W.J. Satellite derived urban heat islands from three coastal cities and the utilisation of such data in urban climatology. Int. J. Remote Sens. 1989, 10, 1699-1720.
99. Weng, Q.; Liu, H.; Lu, D. Assessing the effects of land use and land cover patterns on thermal conditions using landscape metrics in city of Indianapolis, United States. Urban Ecosyst. 2007, 10, 203-219.
100. Liu, H.; Weng, Q. An examination of the effect of landscape pattern, land surface temperature, and socioeconomic conditions on WNV dissemination in Chicago. Environ. Monit. Assess. 2009, 159, 143-161.
101. Hay, G.J.; Hemachandran, B.; Kyle, C.D. HEAT (Home Energy Assessment Technologies): Residential waste heat monitoring, google maps and airborne thermal imagery. Alberta, Canada. GIM Int. 2010, 24, 13-15.
102. Hay, G.J.; Kyle, C.D.; Hemachandran, B.; Chen, G.; Rahman, M.; Fung, T.S.; Arvai, J.L. Geospatial technologies to improve urban energy efficiency. Remote Sens. 2011, 3, 1380-1405.
103. Lu, D.; Weng, Q. Spectral mixture analysis of ASTER imagery for examining the relationship between thermal features and biophysical descriptors in Indianapolis, Indiana. Remote Sens. Environ. 2006, 104, 157-167.
104. Lu, D.; Weng, Q. Use of impervious surface in urban land use classification. Remote Sens. Environ. 2006, 102, 146-160.
105. Weng, Q.; Hu, X.; Liu, H. Estimating impervious surfaces using linear spectral mixture analysis with multi-temporal ASTER images. Int. J. Remote Sens. 2009, 30, 4807-4830.
106. Resch, B.; Lippautz, M.; Mittlboeck, M. Pervasive monitoring-a standardised sensor web approach for intelligent sensing infrastructures. Sensors 2010, 10, 11440-11467.
107. Gross, N. The Earth will don an electronic skin. BusinessWeek Online 30 August 1999. Available online: http://www.businessweek.com/1999/99_35/b3644024.htm (accessed on 20 February 2011).
108. Paulsen, H.; Riegger, U. SensorGIS-Geodaten in Echtzeit. GIS-Bus. 2006, 8, 17-19.
109. Resch, B.; Britter, R.; Ratti, C. Live urbanism-towards the senseable city and beyond. In Sustainable Architectural Design: Impacts on Health; Pardalos, P., Rassia, S., Eds.; 2011; in press.
110. Chong, C.; Kumar, S. Sensor networks: Evolution, opportunities, and challenges. Proc. IEEE 2003, 91, 1247-1256.
111. Lesser, V.; Ortiz, C.; Tambe, M. Distributed Sensor Networks: A Multiagent Perspective; Springer: Berlin, Germany, 2003.
112. De Wolf, T.; Holvoet, T. Towards Autonomic Computing: Agent-Based Modelling, Dynamical Systems Analysis, and Decentralised Control. In Proceedings of the IEEE International Conference on Industrial Informatics, 2003, INDIN 2003, Banff, AB, Canada, 21-24 August 2003; pp. 470-479.
113. Botts, M.; Robin, A.; Davidson, J.; Simonis, I. OpenGIS Sensor Web Enablement Architecture; OpenGIS Discussion Paper OGC 06-021r1; Version 1.0; Open Geospatial Consortium Inc.: Wayland, MA, USA, 4 March 2006.
114. Goodchild, M.F. Communicating geographic information in a digital age. Ann. Assoc. Am. Geogr. 2000, 90, 344-355.
115. Gore, A. The digital earth: Understanding our planet in the 21st Century. Photogramm. Eng. Remote Sensing 1999, 65, 528-530.
116. Craglia, M.; Goodchild, M.F.; Annoni, A.; Camara, G.; Gould, M.; Kuhn, W.; Mark, D.M.; Masser, I.; Maguire, D.J.; Liang, S.; et al. Next-generation digital earth. A position paper from the Vespucci initiative for the advancement of geographic information science. Int. J. Spat. Data Infrastruct. Res. 2008, 3, 146-167.
117. Goodchild, M.F. Citizens as voluntary sensors: Spatial data infrastructure in the world of Web 2.0. Int. J. Spat. Data Infrastruct. Res. 2007, 2, 24-32.
118. Mesev, V. Identification and characterisation of urban building patterns using IKONOS imagery and point-based postal data. Comput. Environ. Urban Syst. 2005, 29, 541-557.
119. Faust, N.L.; Anderson, W.H.; Star, J.L. Geographic information systems and remote sensing future computing environment. Photogramm. Eng. Remote Sensing 1991, 57, 655-668.
120. Kimes, D.S.; Harrison, P.R.; Ratcliffe, P.A. A knowledge-based expert system for inferring vegetation characteristics. Int. J. Remote Sens. 1991, 12, 1987-2020.
121. Steiniger, S.; Hay, G.J. Free and open source geographic information tools for landscape ecology: A review. Ecol. Inf. 2009, 4, 183-195.
122. Gao, J. Integration of GPS with remote sensing and GIS: reality and prospect. Photogramm. Eng. Remote Sensing 2002, 68, 447-453.
123. Karimi, H.A.; Khattak, A.H; Hummer, J.E. Evaluation of mobile mapping systems for roadway data collection. J. Comput. Civil Eng. 2000, 14, 168-173.
124. Xue, Y.; Cracknell, A.P.; Guo, H.D. Telegeoprocessing: The integration of remote sensing, geographic information system (GIS), global positioning system (GPS) and telecommunication. Int. J. Remote Sens. 2002, 23, 1851-1893.
125. Sabins, F.F. Remote Sensing: Principles and Interpretation; W.H. Freeman: New York, NY, USA, 1987.
126. Torrens, P.M. Wi-Fi geographies. Ann. Assoc. Am. Geogr. 2008, 98, 59-84.
127. Goodchild, M.F. Citizens as sensors: The world of volunteered geography. GeoJournal 2007, 69, 211-221.
128. Resch, B.; Mittlboeck, M.; Kranzer, S.; Sagl, G.; Heistracher, T.; Blaschke, T. "People as sensors" mittels personalisertem geo-tracking. In Angewandte Geoinformatik; Strobl, J., Blaschke, T., Griesebner, G., Eds.; Wichmann Verlag: Heidelberg, Germany, 2011; 682-687.
129. Yu, Q.; Gong, P.; Clinton, N.; Biging, G.; Kelly, M.; Schirokauer, D. Object-based detailed vegetation classification with airborne high resolution remote sensing imagery. Photogramm. Eng. Remote Sensing 2006, 72, 799-781.
130. Liu, D.; Kelly, M.; Gong, P. A spatial-temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery. Remote Sens. Environ. 2006, 101, 167-180.
131. Chen, K. Correlations between Census Dwelling Data and Remotely Sensed Data. In Proceedings of the SIRC 98: 10th Annual Colloquium of the Spatial Information Research Centre, Dunedin, New Zealand, 16-19 November 1998.
132. Banzhaf, E.; Kindler, A.; Haase, D. Monitoring and Modelling Indicators for Urban Shrinkage-The City of Leipzig, Germany. In Proceedings of the Second Workshop of the EARSeL SIG on Remote Sensing of Land Use and Land Cover, Bonn, Germany, 28-30 September 2006.
133. Hollands, R.G. Will the real smart city please stand up? Intelligent, progressive or entrepreneurial? City 2008, 12, 303-320.
134. Giffinger, R.; Fertner, C.; Kramar, H.; Meijers, E.; Pichler-Milanović, N. Smart Cities: Ranking of European Medium-Sized Cities; Final Report; Centre of Regional Science, Vienna UT: Vienna, Austria, October 2007. Available online: www.smart-cities.eu/download/smart_cities_final_report.pdf (accessed on 9 August 2011).
135. Partridge, H. Developing a Human Perspective to the Digital Divide in the Smart City. Presented at ALIA 2004, Brisbane, QLD, Australia, 21-24 September 2004. Available online: http://eprints.qut.edu.au/1299/1/partridge.h.2.paper.pdf (accessed on 9 August 2011).
136. Calabrese, F.; Ratti, C. Real time rome. Netw. Commun. Stud. 2006, 20, 247-258.
137. Reades, J., Calabrese, F.; Sevtsuk, A.; Ratti, C. Cellular census: Explorations in urban data collection. Pervasive Comput. 2007, 6, 30-38.
138. Dierig, S.; Lachmund, J.; Mendelsohn, A. Science and the City; Workshop, Max Planck Institute for the History of Science: Berlin, Germany, 1-3 December 2000. Available online: http://vlp.mpiwg-berlin.mpg.de/exp/dierig/science_city.html (accessed on 10 September 2010).
139. Netherlands Organization for Scientific Research. Urban Sciences; Interdisciplinary Research Programme on Urbanization & Urban Culture in The Netherlands, 2007. Available online: http://www.urbansciences.eu (accessed on 26 August 2010).
140. Resch, B. Live Geography-Standardised Geo-sensor Networks for Real-Time Monitoring in Urban Environments. Ph.D. Thesis, University of Salzburg, Salzburg, Austria, December 2009; (unpublished).
141. Resch, B.; Mittlboeck, M.; Girardin, F.; Britter, R.; Ratti, C. Live geography-embedded sensing for standardised urban environmental monitoring. Int. J. Adv. Syst. Meas. 2009, 2, 156-167.
142. Resch, B.; Lippautz, M.; Mittlboeck, M. Pervasive monitoring-a standardised sensor web approach for intelligent sensing infrastructures. Sensors 2010, 10, 11440-11467.
143. Nittel, S.A. Survey of geosensor networks: Advances in dynamic environmental monitoring. Sensors 2009, 9, 5664-5678.
144. Ferscha, A.; Vogl, S.; Beer, W. Context sensing, aggregation, representation and exploitation in wireless networks. Sci. Int. J. Parallel Distrib. Comput. 2005, 6, 77-81.
145. Goodchild, M.F. Citizens as sensors: The world of volunteered geography. GeoJournal 2007, 69, 211-221.
146. Campbell, A.T.; Eisenman, S.B.; Lane, N.D.; Miluzzo, E.; Peterson, R. People-Centric Urban Sensing. In Proceedings of the 2nd ACM/IEEE Annual International Wireless Internet Conference, Boston, MA, USA, 2-5 August 2006.
147. Oke, T.R. Initial Guidance to Obtain Representative Meteorological Observations at Urban Sites; IOM Report No.81, WMO/TD. No. 1250; World Meteorological Organization: Geneva, Switzerland, 2006.
148. Anonymous. Reviewer B, comments to the second submission of Manuscript ID: remotesensing- 8447-Blaschke-at, 15 June 2011.