building extraction and population mapping using high resolution images serkan ural, ejaz hussain,...
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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: 764-494-2168
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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