1. BUILDING EXTRACTION AND POPULATION MAPPING USING HIGH RESOLUTION IMAGES Serkan Ural, Ejaz Hussain, Jie Shan, Associate Professor Presented at the Indiana GIS Conference 2010 {sural,ehussain,jshan}@purdue.edu Tel: 764-494-2168 School of Civil Engineering Purdue University Feb 24, 2010

2. Acknowledgement –Images and elevation data: Indiana View –Building footprints, address data, and zoning maps: Tippecanoe County GIS –Census population data: U.S. Census Bureau 2

3. Outline Objective Population Mapping Study Area and Data Methods Assessment Conclusion 3

4. Objective 4 Urban land cover mapping, especially buildings from high resolution imagery and additional geospatial data using object- based image classification Investigate the applicability of extracted building footprints as a basis for micro-population estimation by disaggregation of population at individual building level

5. Population Mapping 5 Estimation of population distribution at high spatial and temporal resolution is of importance for applications which use spatio- temporal distribution of population together with other physical, social and economic variables Public health Environmental health Urban planning Crime mapping Emergency response planning etc.

6. Population Mapping 6 Census -once in every 10 years -population reported of aggregate zones (e.g. census blocks) -predictions reported annually in township level Estimation of population at finer scales -single housing and apartment units Mapping residential buildings from high resolution images

7. Study Area and Data 7 West Lafayette, IN CIR aerial images (2005) -Resolution-1 m, 4 bands Elevation Data (digital elevation and surface models) -Resolution-5 feet Building footprints (2000) Building address points data City zoning map (scanned) U.S. Census 2000 population (census block level)

8. 8 Test Data CIR 2005 Aerial ImageDSM

9. 9 Test Data Zoning Map-ScannedZoning Map-Digitized Residential planned development Single family residential Single, two and multi-family residential Non-residential planned development Neighborhood business

10. 10 Test Data Address Point DataBuilding Footprints

11. Building Extraction Availability of high resolution images (1 m) –More details of ground objects Urban feature complexity –Different objects with spectral similarity ( Roads, parking lots, walkways, and building roofs) –Similar objects with variable spectral response (Multi color roofs, concrete and bituminous based impervious surfaces) –Similar objects with a variety of shapes and sizes (buildings) –Tree or their shadows covering houses, roads and street 11

12. High Resolution Images and Urban Features Complexity 12

13. Building Extraction Object based image classification -Segmentation: Division of image into homogeneous regions –Classification:  Nearest Neighbor  Fuzzy rules (membership functions) –Use of spectral, contextual and texture features for classification –Sequential classification 13

14. Building extraction within census block group boundaries 14 CIR 2005 Building Extraction

15. Land cover classification 15 WaterBuildings (1, 2) VegetationRoads Parking Lots Shadow TreesGrassResidentialNon Residential Class hierarchy Multi-family house Single family house General Business Apartments

16. Classification Results 16

17. Classification Results 17

18. Classification Results 18

19. Height information (nDSM) derived from Elevation data (DSM – DEM) for separation of elevated and non elevated objects Zoning maps for the categorization of residential and non residential buildings Use of address point data to check and validate the classification of multi family houses based on building (footprints) covered area 19 Classification Results – Buildings

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21. Multifamily houses with less cover area mix up with some of the single family houses with large footprints Address point data can help to separate and correctly classify residential buildings as single and multi family houses 21 Classification Results – Buildings

22. 22 Single family HousesCorrectly classified -Multi family housesMisclassified -Multi family houses Classification Results – Buildings

23. Buildings change detection between year 2000 and 2005 Comparison of county building 2000 footprints with buildings extracted from 2005 high resolution images 23 Classification Results – Buildings

24. 24 NO CHANGE DEMOLISHED MISSED NEW BUILT 2000 Building Footprints (County GIS) 2005 Building Footprints (Image Classification) Classification Results – Buildings

25. 25 TractTypeMissedFalseNewDemolished 51Business1-35 52 Residential1222330 Business1-63 Classification Results – Buildings

26. Classification Results - Buildings 26 Buildings extracted from frequently acquired high resolution images using object based classification techniques may be suitable to be used as supplementary data for Urban planning and development Monitoring urban growth/sprawl Maintaining and updating GIS building layers used for various purposes etc.

27. Identification of Residential Buildings 27 Disaggregate population at individual building level Distribute census population to the residential buildings Filter out the non-residential buildings from initially classified extracted building footprints Use different weights for different building types Refine the classification of buildings as houses and apartment buildings

28. 28 Building extraction Small area non-residential building filtering using address points Area threshold determination for small area non-residential buildings CIR images Filtered small area non- residential buildings Address points Small area non-residential building filtering using area threshold Building footprints Remaining building footprints Zoning maps Residential / non-residential building classification Non-residential buildingsResidential buildings Classify single family and apartment buildings Google Maps & Site Visits Address Points Identification of Residential Buildings

29. 29 Residential planned development Single family residential Single, two and multi-family residential Non-residential planned development Neighbourhood business Zoning Map

30. Identification of Residential Buildings 30 Address Data

31. Dasymetric Mapping of Population 31 Areametric: Volumetric : Weighted Areametric: Weighted Volumetric: Building population Census unit population Building Area (Lwin and Murayama, 2009) Weighting factor Building Volume

32. 2000 Census Population Distribution 32

33. 2000 Census Population Distribution 33 RMSE (2000) Methodn = 89n = 84 Areametric56.8151.05 Weighted Areametric 30.2023.31 Volumetric43.7938.69 Weighted Volumetric34.3824.34

34. U.S. Census Population Predictions 34 Building footprints extracted from 2005 high resolution images U.S. Census Bureau provides annual predictions at township level Extent of the study area is a subset of a township Trend of population change modeled by fitting a 5 th order polynomial to U.S. Census predictions at township level Obtained trend is used to obtain the population of the census blocks in the study area at 2005

35. U.S. Census Population Predictions 35 Year US Census Predicted Population Population Growth Rate (%) 2008546911.158 2007540651.363 2006533382.264 2005521570.892 200451696-0.010 200351701-0.097 2002517511.017 200151230-0.006 200051233

36. 2005 Predicted Population Distribution 36

37. Assessment 37 Tree cover DSM errors Census data problems -Census block boundary alignment -Non-correspondence with existing residential buildings Data integration

38. DSM Errors 38

39. Census 2000 Data Problems 39 Census Block #4001 Census Block #4000 Number of Residential Buildings = 20 Census 2000 Population = 3 Census 2000 Population = 51 Number of Residential Buildings = 1

40. Conclusions 40 Object based image classification is an effective method to extract buildings from high resolution images Integration of elevation data further improves building extraction 98% overall classification accuracy achieved using both high resolution images and elevation data Volumetric method produce better results than areametric method without the inclusion of a weighting factor Inclusion of a weighting factor improves the results for building population estimation Further classification of building types may improve the estimation results

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