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: 764-494-2168

School of Civil EngineeringPurdue University

Feb 24, 2010

AcknowledgementAcknowledgement

– Images and elevation data: Indiana View

– Building footprints, address data, and zoning

maps: Tippecanoe County GIS

– Census population data: U.S. Census Bureau

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OutlineOutline

• Objective

• Population Mapping

• Study Area and Data

• Methods

• Assessment

• Conclusion

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ObjectiveObjective

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• 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 MappingPopulation Mapping

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• 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.

Population MappingPopulation Mapping

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• 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

Study Area and DataStudy Area and Data

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• 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)

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Test DataTest Data

CIR 2005 Aerial Image DSM

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Test DataTest Data

Zoning Map-Scanned Zoning Map-Digitized

Residential planned development

Single family residential Single, two and multi-family residential

Non-residential planned development

Neighborhood business

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Test DataTest Data

Address Point DataBuilding Footprints

Building ExtractionBuilding 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

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High Resolution Images and Urban Features Complexity

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Building ExtractionBuilding 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

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• Building extraction within census block group boundaries

14CIR 2005CIR 2005

Building ExtractionBuilding Extraction

Building ExtractionBuilding Extraction

• Land cover classification

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Water Buildings (1, 2)

VegetationRoads Parking Lots

Shadow

TreesGrassResidentialNon Residential

Class hierarchy

Multi-family house

Single family house

General Business

Apartments

Classification ResultsClassification Results

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Classification ResultsClassification Results

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Classification ResultsClassification Results

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• 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

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Classification Results – Classification Results – BuildingsBuildings

Classification Results – Classification Results – BuildingsBuildings

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• 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

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Classification Results – Classification Results – BuildingsBuildings

22Single family Houses Correctly classified -Multi family houses Misclassified -Multi family houses

Classification Results – Classification Results – BuildingsBuildings

• Buildings change detection between year 2000 and

2005

• Comparison of county building 2000 footprints with

buildings extracted from 2005 high resolution images

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Classification Results – Classification Results – BuildingsBuildings

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NO CHANGEDEMOLISHED

MISSED

NEW BUILT

2000 Building Footprints (County GIS) 2005 Building

Footprints (Image Classification)

Classification Results – Classification Results – BuildingsBuildings

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Tract Type Missed False New Demolished

51 Business 1 - 3 5

52Residential 12 2 23 30

Business 1 - 6 3

Classification Results – Classification Results – BuildingsBuildings

Classification Results - BuildingsClassification Results - Buildings

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• 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.

Identification of Residential Identification of Residential BuildingsBuildings

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• 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

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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

Remaining building footprints

Zoning maps

Residential / non-residential building classification

Non-residential buildings Residential buildings

Classify single family and apartment buildings

Google Maps & Site Visits

Address Points

Identification of Residential Identification of Residential BuildingsBuildings

Identification of Residential Identification of Residential BuildingsBuildings

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Residential planned development

Single family residential Single, two and multi-family

residentialNon-residential planned developmentNeighbourhood business

Zoning MapZoning Map

Identification of Residential Identification of Residential BuildingsBuildings

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Address DataAddress Data

Dasymetric Mapping of Dasymetric Mapping of PopulationPopulation

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Areametric: Volumetric :

Weighted Areametric:

Weighted Volumetric:

Building population

Census unit population

Building Area(Lwin and Murayama, 2009)

Weighting factor

Building Volume

2000 Census Population 2000 Census Population Distribution Distribution

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2000 Census Population 2000 Census Population Distribution Distribution

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RMSE (2000)Method n = 89 n = 84

Areametric

56.81 51.05

Weighted Areametri

c30.20 23.31

Volumetric

43.79 38.69

Weighted Volumetri

c34.38 24.34

U.S. Census Population U.S. Census Population PredictionsPredictions

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• 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

U.S. Census Population U.S. Census Population PredictionsPredictions

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Year

US Census

Predicted Populatio

n

Population Growth Rate (%)

2008 54691 1.1582007 54065 1.3632006 53338 2.2642005 52157 0.8922004 51696 -0.0102003 51701 -0.0972002 51751 1.0172001 51230 -0.0062000 51233

2005 Predicted Population 2005 Predicted Population Distribution Distribution

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AssessmentAssessment

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• Tree cover

• DSM errors

• Census data problems

- Census block boundary alignment

- Non-correspondence with existing residential

buildings

• Data integration

DSM ErrorsDSM Errors

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Census 2000 Data ProblemsCensus 2000 Data Problems

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Census Block #4001

Census Block #4000

Number of Residential Buildings

= 20

Census 2000Population

= 3Census 2000Population

= 51

Number of Residential Buildings

= 1

ConclusionsConclusions

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• 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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