Automatic identification of building types based on topographic databases–a comparison of different data sources

International Journal of Cartography

Volumn 1, Issue 1, 2015, Pages 18-31

  Hecht, Robert ^a   Meinel, Gotthard ^a   Buchroithner, Manfred ^b  

^a LEIBNIZ INSTITUTE OF ECOLOGICAL URBAN AND REGIONAL DEVELOPMENT   (Germany)

^b DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

Building footprints; classification; machine learning; settlement structure

Indexed keywords

EID: 84994373849     PISSN: None     EISSN: 23729341     Source Type: Journal    
DOI: 10.1080/23729333.2015.1055644     Document Type: Article

References (46)

1. Alpaydin, E., (2004). Introduction to machine learning. Cambridge MA and London: The MIT Press.
2. Anders, K.-H., Sester, M., & Fritsch, D., (1999). Analysis of settlement structures by graph-based clustering. SMATI 99 workshop on ‘semantic modeling’. München, pp. 41–49.
3. Aubrecht, C., Steinnocher, K., Hollaus, M., & Wagner, W., (2009). Integrating earth observation and GIScience for high resolution spatial and functional modeling of urban land use. Computers, Environment and Urban Systems, 33(1), 15–25. doi: 10.1016/j.compenvurbsys.2008.09.007
4. Banzhaf, E., & Höfer, R., (2008). Monitoring Urban structure types as spatial indicators with CIR aerial photographs for a more effective Urban environmental management. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1(2), 129–138. doi: 10.1109/JSTARS.2008.2003310
5. Barr, S. L., & Barnsley, M. J., (1997). A region-based, graph-theoretic data model for the inference of second-order thematic information from remotely-sensed images. International Journal of Geographical Information Science, 11(6), 555–576. doi: 10.1080/136588197242194
6. Batty, M., (2007). Planning support systems: Progress, predictions, and speculations on the shape of things to come (CASA Working Paper Series). London: Centre for advanced spatial analysis.
7. Belgiu, M., Tomljenovic, I., Lampoltshammer, T. J., Blaschke, T., & Höfle, B., (2014). Ontology-based classification of building types detected from airborne laser scanning data. Remote Sensing, 6(2), 1347–1366. doi: 10.3390/rs6021347
8. BPIE. (2011). Europe's buildings under the microscope. A country-by-country review of the energy performance of buildings. Brussels: Buildings Performance Institute Europe.
9. Breiman, L., (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
10. Breiman, L., (2001). Random forests. Machine Learning, 45(1), 5–32. doi: 10.1023/A:1010933404324
11. Buchert, M., Fritsche, U., Jenseit, W., Rausch, L., Deilmann, C., Schiller, G., Siedentop, S., et al., Öko-Institut. (Eds.). (2004). Nachhaltiges Bauen und Wohnen in Deutschland: Stoffflussbezogene Bausteine für ein nationales Konzept der nachhaltigen Entwicklung, UBA-Texte 01/2004. Umweltbundesamt.
12. Burghardt, D., & Steiniger, S., (2005, July 11–16). Usage of principal component analysis in the process of automated generalisation. Proceedings of the 22nd International Cartographic Conference, A Coruña, Spain.
13. Clifton, K., Ewing, R., Knaapa, G.-J., & Song, Y., (2008). Quantitative analysis of urban form: A multidisciplinary review. Journal of Urbanism: International Research on Placemaking and Urban Sustainability, 1(1), 17–45.
14. Congalton, R. G., & Green, K., (1998). Assessing the accuracy of remotely sensed data: Principles and practices. Oxford: CRC Press.
15. Geiß, C., Taubenböck, H., Wurm, M., Esch, T., Nast, M., Schillings, C., & Blaschke, T., (2011). Remote sensing-based characterization of settlement structures for assessing local potential of district heat. Remote Sensing, 3(7), 1447–1471. doi: 10.3390/rs3071447
16. Hecht, R., (2014). Automatische Klassifizierung von Gebäudegrundrissen–Ein Beitrag zur kleinräumigen Beschreibung der Siedlungsstruktur (Dissertation, Dresden University of Technology. IÖR-Schriften 63). Berlin: Rhombos.
17. Hecht, R., Kunze, C., & Hahmann, S., (2013). Measuring completeness of building footprints in OpenStreetMap over space and time. ISPRS International Journal of Geo-Information, 2(4), 1066–1091. doi: 10.3390/ijgi2041066
18. Henn, A., Römer, C., Gröger, G., & Plümer, L., (2012). Automatic classification of building types in 3D city models Using SVMs for semantic enrichment of low resolution building data. GeoInformatica, 16(2), 281–306. doi: 10.1007/s10707-011-0131-x
19. Herold, H., (2015). An evolutionary approach to adaptive image analysis for retrieving and long-term monitoring historical land use from spatiotemporally heterogeneous map sources (Doctoral thesis). Dresden: Dresden University of Technology.
20. Herold, H., Meinel, G., Hecht, R., & Csaplovics, E., (2012, May 7–9). A GEOBIA approach to map interpretation–multitemporal building footprint retrieval for high resolution monitoring of spatial Urban dynamics. International conference on geographic object-based image analysis, Rio de Janeiro, Brazil.
21. Institut Wohnen und Umwelt. (2003). Deutsche Gebäudetypologie - Systematik und Datensätze. Darmstadt: Author.
22. Jolliffe, I. T., (1973). Discarding variables in a principal component analysis. II: Real data. Journal of the Royal Statistical (Series C), 22(1), 21–31.
23. Krüger, T., Meinel, G., & Schumacher, U., (2013). Land-use monitoring by topographic data analysis. Cartography and Geographic Information Science, 40(3), 220–228. doi: 10.1080/15230406.2013.809232
24. Liaw, A., & Wiener, M., (2002). Classification and regression by randomForest. R News, 2(3), 18–22.
25. Luft, H., & Bender, G., (1998). Fachwörterbuch: Benennungen und Definitionen im Deutschen. Vermessungswesen mit englischen und französischen Äquivalenten/Stadtplanung, Raumordnung. Bundesamt f. Kartographie u. Geodäsie.
26. Lüscher, P., Weibel, R., & Burghardt, D., (2009). Integrating ontological modelling and Bayesian inference for pattern classification in topographic vector data. Computers, Environment and Urban Systems, 33(5), 363–374. doi: 10.1016/j.compenvurbsys.2009.07.005
27. Maantay, J., & Maroko, A., (2009). Mapping urban risk: Flood hazards, race, & environmental justice in New York. Applied Geography, 29(1), 111–124. doi: 10.1016/j.apgeog.2008.08.002
28. Meinel, G., Hecht, R., & Herold, H., (2009). Analyzing building stock using topographic maps and GIS. Building Research & Information, 37(5/6), 468–482. doi: 10.1080/09613210903159833
29. Mesev, V., (2005). Identification and characterisation of urban building patterns using IKONOS imagery and point-based postal data. Computers Environment and Urban Systems, 29(5), 541–557. doi: 10.1016/j.compenvurbsys.2005.01.003
30. Müller, W., & Korda, M., (1999). Städtebau. Stuttgart and Leipzig: Teubner.
31. Neidhart, H., & Sester, M., (2004). Identifying building types and building clusters using 3D-Laser scanning and GIS-Data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 35(Part B4), 715–720.
32. Orford, S., & Radcliffe, J., (2007). Modelling UK residential dwelling types using OS Mastermap data: A comparison to the 2001 census. Computers, Environment and Urban Systems, 31(2), 206–227. doi: 10.1016/j.compenvurbsys.2006.08.003
33. Raheja, J. L., (2005). Recognition of 3D settlement structure for generalization (Dissertation). Munich: Technische Universität München.
34. Regnauld, N., (2001). Contextual building typification in automated map generalization. Algorithmica, 30(2), 312–333. doi: 10.1007/s00453-001-0008-8
35. Römer, C., & Plümer, L., (2010). Identifying architectural style in 3D city models with support vector machines. Photogrammetrie–Fernerkundung–Geoinformation, 5, 371–384. doi: 10.1127/1432-8364/2010/0063
36. Schalkoff, J. R., (2008). Pattern recognition. In B. W., Wah (Ed.), Wiley encyclopedia of computer science and engineering. New Jersey: John Wiley & Sons.
37. Sester, M., (2000). Knowledge acquisition for the automatic interpretation of spatial data. International Journal of Geographical Information Science, 14(1), 1–24. doi: 10.1080/136588100240930
38. Smith, D., & Crooks, A., (2010). From buildings to cities: Techniques for the multi-scale analysis of urban form and function (CASA Working Papers 155). London: Centre for Advanced Spatial Analysis (UCL).
39. Steiniger, S., Lange, T., Burghardt, D., & Weibel, R., (2008). An approach for the classification of urban building structures based on discriminant analysis techniques. Transactions in GIS, 12(1), 31–59. doi: 10.1111/j.1467-9671.2008.01085.x
40. Theodoridis, S., & Koutroumbas, K., (2008). Pattern recognition (4th ed.). San Diego: Academic Press.
41. Thomson, M.-K., (2009). Dwelling on ontology - semantic reasoning over topographic maps (Doctoral thesis). London: University College London.
42. Vanderhaegen, S., & Canters, F., (2010). Developing Urban metrics to describe the morphology of Urban areas at block level. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(4/C7), 192–197.
43. Walde, I., Hese, S., Berger, C., & Schmullius, C., (2012) Graph-based mapping of urban structure types from high-resolution satellite image objects–case study of the German cities Rostock and Erfurt. IEEE Geoscience and Remote Sensing Letters, 10(4), 932–936. doi: 10.1109/LGRS.2013.2252323
44. Werder, S., Kieler, B., & Sester, M., (2010). Semi-automatic interpretation of buildings and settlement areas in user-generated spatial data. In Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems (pp. 330–339). New York: ACM.
45. Wu, S., Xu, B., & Wang, L., (2006). Urban land-use classification using variogram-based analysis with an aerial photograph. Photogrammetric Engineering and Remote Sensing, 72(7), 813–822. doi: 10.14358/PERS.72.7.813
46. Wurm, M., Taubenböck, H., Roth, A., & Dech, S., (2009) Urban structuring using multisensoral remote sensing data: By the example of the German cities Cologne and Dresden. Shanghai: Joint Urban Remote Sensing Event.