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BUILDING EXTRACTION AND POPULATION MAPPING USING HIGH RESOLUTION IMAGES

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.

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BUILDING EXTRACTION AND POPULATION MAPPING USING HIGH RESOLUTION IMAGES

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  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 • 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 4

  5. Population Mapping • 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. 5

  6. Population Mapping • 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 6

  7. Study Area and Data • 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) 7

  8. Test Data CIR 2005 Aerial Image DSM 8

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

  10. Test Data Building Footprints Address Point Data 10

  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 Building Extraction 14 CIR 2005

  15. Building Extraction Land cover classification Water Buildings (1, 2) Shadow Roads Parking Lots Vegetation Non Residential Residential Grass Trees Class hierarchy Single family house Multi-family house Apartments General Business 15

  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 Classification Results – Buildings 19

  20. Classification Results – Buildings 20

  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 Classification Results – Buildings 21

  22. Classification Results – Buildings 22 Single family Houses Correctly classified -Multi family houses Misclassified -Multi family houses

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

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

  25. Classification Results – Buildings 25

  26. Classification Results - Buildings • 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. 26

  27. Identification of Residential Buildings • 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 27

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

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

  30. Identification of Residential Buildings Address Data 30

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

  32. 2000 Census Population Distribution 32

  33. 2000 Census Population Distribution 33

  34. U.S. Census Population Predictions • 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 5th 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 34

  35. U.S. Census Population Predictions 35

  36. 2005 Predicted Population Distribution 36

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

  38. DSM Errors 38

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

  40. Conclusions • 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 40

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