BUILDING EXTRACTION AND POPULATION MAPPING USING HIGH RESOLUTION IMAGES

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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. - PowerPoint PPT Presentation

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  • 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.eduTel: 764-494-2168School of Civil EngineeringPurdue UniversityFeb 24, 2010

  • Acknowledgement

    Images and elevation data: Indiana ViewBuilding footprints, address data, and zoning maps: Tippecanoe County GISCensus population data: U.S. Census Bureau*

  • Outline

    ObjectivePopulation MappingStudy Area and DataMethodsAssessmentConclusion*

  • 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

  • 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 variablesPublic healthEnvironmental healthUrban planningCrime mappingEmergency response planning etc.

  • Population Mapping

    *Censusonce in every 10 yearspopulation reported of aggregate zones (e.g. census blocks)predictions reported annually in township levelEstimation of population at finer scalessingle housing and apartment unitsMapping residential buildings from high resolution images

  • Study Area and Data*West Lafayette, INCIR aerial images (2005) Resolution-1 m, 4 bandsElevation Data (digital elevation and surface models)Resolution-5 feetBuilding footprints (2000) Building address points dataCity zoning map (scanned)U.S. Census 2000 population (census block level)

  • *Test DataCIR 2005 Aerial ImageDSM

  • *Test DataZoning Map-ScannedZoning Map-Digitized

  • *Test DataAddress Point DataBuilding Footprints

  • 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

    *

  • High Resolution Images and Urban Features Complexity

    *

  • Building Extraction

    Object based image classificationSegmentation: Division of image into homogeneous regionsClassification:Nearest NeighborFuzzy rules (membership functions)Use of spectral, contextual and texture features for classificationSequential classification*

  • Building extraction within census block group boundaries*CIR 2005Building Extraction

  • Building Extraction

    Land cover classification

    *

  • Classification Results*

  • Classification Results*

  • Classification Results*

  • Height information (nDSM) derived from Elevation data (DSM DEM) for separation of elevated and non elevated objectsZoning 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

  • Classification Results Buildings*

  • 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

  • *Single family HousesCorrectly classified -Multi family housesMisclassified -Multi family housesClassification Results Buildings

  • Buildings change detection between year 2000 and 2005Comparison of county building 2000 footprints with buildings extracted from 2005 high resolution images

    *Classification Results Buildings

  • *2000 Building Footprints (County GIS)2005 Building Footprints (Image Classification)Classification Results Buildings

  • *Classification Results Buildings

    TractTypeMissedFalseNewDemolished51Business1-3552Residential1222330Business1-63

  • 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 forUrban planning and developmentMonitoring urban growth/sprawlMaintaining and updating GIS building layers used for various purposes etc.

  • Identification of Residential Buildings*Disaggregate population at individual building levelDistribute census population to the residential buildingsFilter out the non-residential buildings from initially classified extracted building footprintsUse different weights for different building typesRefine the classification of buildings as houses and apartment buildings

  • *Identification of Residential Buildings

  • Identification of Residential Buildings*Zoning Map

  • Identification of Residential Buildings*Address Data

  • Dasymetric Mapping of Population*

    Areametric: Volumetric :

    Weighted Areametric:

    Weighted Volumetric:

    Building populationCensus unit populationBuilding Area(Lwin and Murayama, 2009)Weighting factorBuilding Volume

  • 2000 Census Population Distribution *

  • 2000 Census Population Distribution *

    RMSE (2000)Methodn = 89n = 84Areametric56.8151.05Weighted Areametric30.2023.31Volumetric43.7938.69Weighted Volumetric34.3824.34

  • U.S. Census Population Predictions*

    Building footprints extracted from 2005 high resolution imagesU.S. Census Bureau provides annual predictions at township levelExtent of the study area is a subset of a townshipTrend of population change modeled by fitting a 5th order polynomial to U.S. Census predictions at township levelObtained trend is used to obtain the population of the census blocks in the study area at 2005

  • U.S. Census Population Predictions*

    YearUS Census Predicted PopulationPopulation Growth Rate (%)2008546911.1582007540651.3632006533382.2642005521570.892200451696-0.010200351701-0.0972002517511.017200151230-0.006200051233

  • 2005 Predicted Population Distribution *

  • Assessment*Tree coverDSM errorsCensus data problemsCensus block boundary alignmentNon-correspondence with existing residential buildingsData integration

  • DSM Errors*

  • Census 2000 Data Problems*Census Block #4001Census Block #4000

  • Conclusions*Object based image classification is an effective method to extract buildings from high resolution imagesIntegration of elevation data further improves building extraction98% overall classification accuracy achieved using both high resolution images and elevation dataVolumetric method produce better results than areametric method without the inclusion of a weighting factorInclusion of a weighting factor improves the results for building population estimationFurther classification of building types may improve the estimation results

  • *

    *********************************************************************************

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