Extracting Building Footprints Extracting Building Footprints from LiDAR and Aerial Imagery in from LiDAR and Aerial Imagery in

the Wildland Urban Interface the Wildland Urban Interface (WUI)(WUI)

Derek McNamaraDerek McNamara

GIS Analyst GIS Analyst

Coeur D’Alene TribeCoeur D’Alene Tribe

Presentation OverviewPresentation Overview

Goals & ObjectivesGoals & Objectives Available DataAvailable Data Examined MethodsExamined Methods Accuracy AssessmentsAccuracy Assessments

– MethodsMethods– ResultsResults

Discussion (Limitations & Challenges)Discussion (Limitations & Challenges) Future WorkFuture Work

BackgroundBackground

Catastrophic Fires in WUICatastrophic Fires in WUI– ~9,000 homes destroyed 1985-1994 (NFPA)~9,000 homes destroyed 1985-1994 (NFPA)

Gain Understanding WUI Fire BehaviorGain Understanding WUI Fire Behavior Few Physics-Based Models of WUI FiresFew Physics-Based Models of WUI Fires Cooperative ProjectCooperative Project

– CDA Tribe & National Institute for Standards & Technology CDA Tribe & National Institute for Standards & Technology – Modification of Fire Dynamics Simulator (FDS) for WUI Modification of Fire Dynamics Simulator (FDS) for WUI

CDA Tribe Provides Model InputsCDA Tribe Provides Model Inputs– Structure InformationStructure Information

– Tree Stem Locations (Crown Width, Height, Height to Live Crown) Tree Stem Locations (Crown Width, Height, Height to Live Crown)

– VegetationVegetation

– Fire Barriers Fire Barriers

Goals & ObjectivesGoals & Objectives

1)1) Extract Building FootprintsExtract Building Footprints– Entire CDA Tribe ReservationEntire CDA Tribe Reservation– Inputs to FDS for testingInputs to FDS for testing

2)2) Compare MethodologiesCompare Methodologies– Conclusions on FeasibilityConclusions on Feasibility– Identify/Develop Robust MethodologyIdentify/Develop Robust Methodology

Available DataAvailable Data

LiDAR Coverage (Entire Reservation)LiDAR Coverage (Entire Reservation) Multispectral Imagery (NAIP)Multispectral Imagery (NAIP) Structure LocationsStructure Locations

– Surveyed NFPA 1144 Assessment FormSurveyed NFPA 1144 Assessment Form

Examined MethodsExamined Methods

Building ExtractionBuilding Extraction– Many Methods Conducted in Urban AreasMany Methods Conducted in Urban Areas– Fewer studies in Rural Areas.Fewer studies in Rural Areas.

4 Methods Examined in WUI4 Methods Examined in WUI

Initial LiDAR ProcessingInitial LiDAR Processing

DerivenDSM

All ReturnLiDAR

Point Data

Bare EarthLiDARDEM

ExtractValues

To Points

CalculateSurfaceHeight

SurfaceHeight

Threshold

Bare Earth DEMBare Earth DEM– AML: Evans (2005, RMRS)AML: Evans (2005, RMRS)– Create TIN Ground PointsCreate TIN Ground Points– TIN to Raster (1 meter)TIN to Raster (1 meter)

Last Return PointsLast Return Points– Threshold (2 – 15 meters)Threshold (2 – 15 meters)

Points Outside ThresholdPoints Outside Threshold– Set to ZeroSet to Zero

– Create TIN from HeightsCreate TIN from Heights– TIN to Raster (1 meter)TIN to Raster (1 meter)

LastReturn

Threshold

Texture Measure ExtractionTexture Measure Extraction

Common Method in LiteratureCommon Method in Literature– Maas (1999); ICREST (2001); etc..Maas (1999); ICREST (2001); etc..

Local Variations of HeightLocal Variations of Height Edge Detectors, Texture, Slope, Curvature, Edge Detectors, Texture, Slope, Curvature,

Etc…Etc…– Similar ResultsSimilar Results

Present Results Texture VariancePresent Results Texture Variance– Incorporate Curvature & SlopeIncorporate Curvature & Slope

Texture Variance ExtractionTexture Variance ExtractionWorkflowWorkflow

nDSMLiDAR

TextureVariance

(3X3)

Vector-ization

AggregatePolygons

SquarePolygons

TextureThreshold(Binary)

AreaThreshold

ThicknessThreshold

FinalBuilding

Polygons

VectorizationVectorization– Binary Image Raster to PolygonBinary Image Raster to Polygon– Delete Largest PolygonDelete Largest Polygon

Aggregate PolygonsAggregate Polygons– Feature AnalystFeature Analyst– Remove Isolated PolygonsRemove Isolated Polygons

Square PolygonsSquare Polygons– Feature AnalystFeature Analyst– Douglas-PeuckerDouglas-Peucker

ThicknessThickness– Zonal GeometryZonal Geometry

Curvature & SlopeCurvature & Slope(Rottensteiner & Briese, 2002)(Rottensteiner & Briese, 2002)

– Apply Threshold (reclassify)Apply Threshold (reclassify)– Count Pixels in Polygon Count Pixels in Polygon – Delete polygons w/ > 50%Delete polygons w/ > 50%

CurvatureThreshold

SlopeThreshold

Height Threshold Workflow Height Threshold Workflow Hewett (2005 ESRI UC)Hewett (2005 ESRI UC)

nDSMLiDAR

ConvertTo

Integer

Vector-ization

AggregatePolygons

SquarePolygons

ReclassifyTo

Binary

AreaThreshold

ThicknessThreshold

FinalBuilding

Polygons

Integer ConversionInteger Conversion– Removes Interpolation ErrorsRemoves Interpolation Errors

CurvatureThreshold

SlopeThreshold

Object Oriented Image ClassificationObject Oriented Image Classification(Ibrahim, 2005) (Ibrahim, 2005)

Related Pixels part of objects.Related Pixels part of objects. Assigns Relationships Related PixelsAssigns Relationships Related Pixels

– Not a Pixel-by-Pixel ApproachNot a Pixel-by-Pixel Approach– Appropriate for Urban ClassificationAppropriate for Urban Classification

(Wikipedia, 2005)(Wikipedia, 2005)

Implemented in Feature AnalystImplemented in Feature Analyst Multispectral & LiDAR HeightMultispectral & LiDAR Height LiDAR Intensity & HeightLiDAR Intensity & Height

Multispectral & LiDAR WorkflowMultispectral & LiDAR Workflow

AggregatePolygons

SquarePolygons

AreaThreshold

ThicknessThreshold Final

BuildingPolygons

PCA 1NAIP

Imagery

TextureVariance

nDSM

SlopenDSM

CurvaturenDSM

TrainingExamples

Set-Up Learning

ApplyHeightMask

ImageClassification

First PCAFirst PCA– Reduce BandsReduce Bands

Set-Up LearningSet-Up Learning– Input RepresentationInput Representation– Spatial ContextSpatial Context

Manhattan 7X7Manhattan 7X7

Height MaskHeight Mask– Improves ClassificationImproves Classification

LiDAR Intensity WorkflowLiDAR Intensity Workflow

AggregatePolygons

SquarePolygons

AreaThreshold

ThicknessThreshold Final

BuildingPolygons

LiDARnDSM

Intensity

TextureVariance

nDSM

SlopenDSM

CurvaturenDSM

TrainingExamples

Set-Up Learning

ApplyHeightMask

ImageClassification

Intensity Not PCAIntensity Not PCA– Measure of Signal Measure of Signal

StrengthStrength– Often contains Often contains

noisenoise

Accuracy Assessment MethodologyAccuracy Assessment Methodology(Song & Haithcoat, 2005)(Song & Haithcoat, 2005)

10 measures Described by Song & Haithcoat 10 measures Described by Song & Haithcoat (2005)(2005)

Use all except Shape SimilarityUse all except Shape Similarity Most Measures Calculated on Correctly Extracted Most Measures Calculated on Correctly Extracted

Building PolygonBuilding Polygon Average Across all Correctly Extracted Building Average Across all Correctly Extracted Building

PolygonPolygon Reference DataReference Data

– Manually DigitizedManually Digitized NAIP, LiDAR, Structure Photos.NAIP, LiDAR, Structure Photos.

Accuracy Assessment MethodologyAccuracy Assessment MethodologyCont.Cont.

Detection Rate = Producer’s AccuracyDetection Rate = Producer’s Accuracy Correctness = User’s AccuracyCorrectness = User’s Accuracy Matched Overlay (Correct Buildings) =Matched Overlay (Correct Buildings) =

Area Omission Error (Correct Buildings) =Area Omission Error (Correct Buildings) =

buildingscorrect number total

area building reference

area building goverlappin

buildingscorrect number total

area building referencearea building dnondetecte

Accuracy Assessment MethodologyAccuracy Assessment MethodologyCont.Cont.

Area Commission Error (Correct Buildings) =Area Commission Error (Correct Buildings) =

RMSE (Correct Buildings) =RMSE (Correct Buildings) =

buildingscorrect number total

area building referencearea building detectedy incorrectl

buildingscorrect number total

buildingcorrect corners #

2^d

Accuracy Assessment MethodologyAccuracy Assessment MethodologyCont.Cont.

Corner Difference (Correct Buildings) =Corner Difference (Correct Buildings) =

Area Difference (Correct Buildings) = Area Difference (Correct Buildings) =

Perimeter Difference (Correct Buildings) =Perimeter Difference (Correct Buildings) =

buildingscorrect ofnumber total

area building reference

area building reference - area building detected

buildingscorrect number total

corners building referencecorners building detected

buildingscorrect ofnumber total

perimeter building reference

perimeter building reference -perimeter building detected

Accuracy AssessmentAccuracy Assessment

Can Not Discern Statistical DifferencesCan Not Discern Statistical Differences Should Look at Methods Building Verse Should Look at Methods Building Verse

BuildingBuilding Identify Common Extracted Building Between Identify Common Extracted Building Between

MethodsMethods Examine statistical differences among methods.Examine statistical differences among methods.

Accuracy Assessment Accuracy Assessment Completeness MeasuresCompleteness Measures

MEASUREMEASURE TextureTexture

ExtractionExtraction

HeightHeight

ExtractionExtraction

Multispectral Multispectral

ExtractionExtraction

LiDAR IntensityLiDAR Intensity

ExtractionExtraction

Detection Rate (%)Detection Rate (%) 69.769.7 73.573.5 72.372.3 66.766.7Correctness (%)Correctness (%) 16.916.9 19.019.0 28.028.0 12.412.4Average Matched Average Matched Overlay (%)Overlay (%) 80.680.6 83.683.6 79.079.0 79.579.5Average Area Average Area Omission Error (%) Omission Error (%) 19.519.5 16.416.4 21.021.0 20.120.1Average Area Average Area Commission Error (%)Commission Error (%) 19.219.2 19.319.3 11.311.3 13.113.1

Extracted Building (Multispectral)

Reference Building

Accuracy Assessment Geometric AccuracyAccuracy Assessment Geometric Accuracy

MEASUREMEASURE TextureTexture

ExtractionExtraction

HeightHeight

ExtractionExtraction

Multispectral Multispectral

ExtractionExtraction

LiDAR IntensityLiDAR Intensity

ExtractionExtraction

Average RMSE (m)Average RMSE (m) 2.022.02 1.901.90 2.032.03 2.402.40Average Corner Average Corner Difference (#)Difference (#) 1.41.4 1.591.59 1.511.51 2.012.01

Treed Area

Reference Buildings

Extracted Buildings (Intensity)

Last Return Intensity Brightness Values0 - 74

74.00000001 - 123

123.0000001 - 160

160.0000001 - 198

198.0000001 - 255

Accuracy Assessment Accuracy Assessment Shape SimilarityShape Similarity

MEASUREMEASURE TextureTexture

ExtractionExtraction

HeightHeight

ExtractionExtraction

Multispectral Multispectral

ExtractionExtraction

LiDAR IntensityLiDAR Intensity

ExtractionExtraction

Average Corner Average Corner Difference (#)Difference (#) 1.41.4 1.591.59 1.511.51 2.012.01Average Area Average Area Difference (%)Difference (%) 19.719.7 22.022.0 19.419.4 20.120.1Average Perimeter Average Perimeter Difference (%)Difference (%) 11.111.1 14.214.2 12.612.6 13.013.0

Texture Variance MeasureTexture Variance Measure– Lowest Corner DifferenceLowest Corner Difference– Lowest Perimeter DifferenceLowest Perimeter Difference

Best Shape Representation (?)Best Shape Representation (?)

Accuracy AssessmentAccuracy AssessmentMEASUREMEASURE TextureTexture

ExtractionExtraction

HeightHeight

ExtractionExtraction

Multispectral Multispectral

ExtractionExtraction

LiDAR IntensityLiDAR Intensity

ExtractionExtraction

Correctness (%)Correctness (%) 16.916.9 19.019.0 28.028.0 12.412.4

Slope & Curvature FiltersSlope & Curvature Filters– Show PromiseShow Promise

Remove ~ 40% of Incorrect PolygonsRemove ~ 40% of Incorrect Polygons

– After Area & Thickness ThresholdsAfter Area & Thickness Thresholds

– Not Removing Buildings Under TreesNot Removing Buildings Under Trees

– Coarse ApproachCoarse Approach More Advanced Approach Better (?)More Advanced Approach Better (?)

Objected-Oriented Approach Objected-Oriented Approach (Slope & Curve Filters)(Slope & Curve Filters) Remove ~ 21% of Incorrect Polygons (Intensity)Remove ~ 21% of Incorrect Polygons (Intensity) Remove ~20% of Incorrect Polygons (Multispectral)Remove ~20% of Incorrect Polygons (Multispectral)

Limitations of All MethodsLimitations of All Methods Lose of Data (Interpolate Point Cloud to Raster)Lose of Data (Interpolate Point Cloud to Raster) Only Rectangular BuildingsOnly Rectangular Buildings Smaller Structures Not DiscernableSmaller Structures Not Discernable Height Thresholds Vary Over Different AreasHeight Thresholds Vary Over Different Areas Poor Job of Removing TreesPoor Job of Removing Trees Last Return Last Return DOES NOTDOES NOT Detect Building Edge Detect Building Edge

Last Return NecessaryLast Return Necessary(Buildings Surrounded by Trees)(Buildings Surrounded by Trees)

Trees Present

First ReturnTrees Filtered

Last Return

Building Extraction ResultsBuilding Extraction ResultsEntire ReservationEntire Reservation

Utilizing All MethodsUtilizing All Methods Database Of Over 11,000 FootprintsDatabase Of Over 11,000 Footprints Structure Point Locations (NFPA Surveys)Structure Point Locations (NFPA Surveys)

– Remove NoiseRemove Noise

Required Manual Clean-upRequired Manual Clean-up– Smaller StructuresSmaller Structures– Densely Canopied AreasDensely Canopied Areas– Modification of Extracted FootprintsModification of Extracted Footprints

Large Area ExtractionLarge Area ExtractionChallengesChallenges

Software LimitationsSoftware Limitations– Large DatasetLarge Dataset– Vectorization RoutinesVectorization Routines

Registration of NAIP to LiDARRegistration of NAIP to LiDAR– Difficult in Forested AreasDifficult in Forested Areas

Varying Height ThresholdsVarying Height Thresholds Different Techniques (First Return Vs Last Return)Different Techniques (First Return Vs Last Return)

– Opened Versus Treed AreasOpened Versus Treed Areas Trees!!! Trees!!!

ConclusionsConclusions LiDAR FeasibleLiDAR Feasible Did Not Quantify Difference Between MethodsDid Not Quantify Difference Between Methods Multispectral Removes Most NoiseMultispectral Removes Most Noise

– Does Not Discern Buildings Under TreesDoes Not Discern Buildings Under Trees LiDAR Intensity Too Much Noise (?)LiDAR Intensity Too Much Noise (?) Height Easy/Good ResultsHeight Easy/Good Results Texture Best Shape Similarity (?)Texture Best Shape Similarity (?) Slope & Curvature Filters Show PromiseSlope & Curvature Filters Show Promise Object-Oriented ApproachObject-Oriented Approach

– No Filter ThresholdsNo Filter Thresholds All Methods Useful in Open AreasAll Methods Useful in Open Areas

– Easy to ApplyEasy to Apply Sensor LimitationsSensor Limitations

– Small Structures Not DiscernableSmall Structures Not Discernable Feature AnalystFeature Analyst

– Easy to UseEasy to Use– Hierarchical Learning Not ExaminedHierarchical Learning Not Examined

Future WorkFuture WorkGeneralGeneral

Better SoftwareBetter Software– Handle Large Point CloudHandle Large Point Cloud

Development of LiDAR StandardsDevelopment of LiDAR Standards– WUI WorkWUI Work

Incorporates Many Extraction ScenariosIncorporates Many Extraction Scenarios– Good Test BedGood Test Bed

Future WorkFuture WorkCDA TribeCDA Tribe

Segment Man-made Objects From Vegetation!!!Segment Man-made Objects From Vegetation!!!– Slope & Curvature (Point Data)Slope & Curvature (Point Data)

Building Extraction Point DataBuilding Extraction Point Data– Plane FittingPlane Fitting

Other Building InformationOther Building Information– Roof TypeRoof Type– HeightHeight

Accuracy AssessmentsAccuracy Assessments– Incorporate Shape SimilarityIncorporate Shape Similarity– Statistically Test Differences Between MethodsStatistically Test Differences Between Methods

AcknowledgementsAcknowledgements

NIST: Ruddy Mell, et al. (Funding)NIST: Ruddy Mell, et al. (Funding) Jeremy Adams, Noel Sanyal (Building Jeremy Adams, Noel Sanyal (Building

Clean-up)Clean-up) Berne Jackson (Systems Manager)Berne Jackson (Systems Manager) Frank Roberts (GIS Manager)Frank Roberts (GIS Manager) James Twoteeth, Heather Fuller (CDA GIS)James Twoteeth, Heather Fuller (CDA GIS) Josh Arnold (NFPA Surveys)Josh Arnold (NFPA Surveys) USGS (Feature Analyst)USGS (Feature Analyst)

Questions?Questions?

Light Detection and Ranging Light Detection and Ranging (LiDAR)(LiDAR)11

Flight Height ~1829 meters

Coverage Area 129,500 hectares

Field of View 25 degrees

Vertical Accuracy 15 centimeters

Horizontal Accuracy 10 centimeters

Returns Per Pulse 5

Line Spacing 1,862 Feet

Maximum Along Track Spacing 1.8 meters

Maximum Cross Track Spacing 2.6 meters

Nominal Post Spacing 2.0 meters

~Number of Elevation Points 347,000,000

Number of Basestation Locations 11Flown by Horizons Inc., South Dakota

Accuracy AssessmentAccuracy AssessmentCont.Cont.

Height Misses

Small StructureDuplicate Structure

Outliers

ReferencesUnited States Department of Agriculture. Forest Service. Rocky Mountain Research Station. Fuels Planning: Science Synthesis and Integration. Environmental Consequences Fact Sheet: 3. Structure Fires in the Wildland-Urban Interface. 2004-09. Research Note RMRS-RN-23-3-WWW.Evans, J.S., and A.T. Hudak. A Progressive Curvature Filter for Identifying Ground Returns from Discrete Return LiDAR in Forested Environments. (Submitted IEEE Transactions on Geoscience and Remote Sensing).Haithcoat, T., and W. Song, J. Hipple. Automated Building Extraction and Reconstruction from LIDAR Data. R&D Program for NASA/ICREST Studies Project Report. September, 2001.Hewett, M. Automating Feature Extraction with the ArcGIS Spatial Analyst Extension. 2005 ESRI International User Conference Proceedings.Mass, G-H. The Potential of Height Texture Measures for the Segmentation of Airborne Laserscanner Data. Presented at the 4th Airborne Remote Sensing Conference and Exhibition, Ottawa, Ontario, Canada, 21-24 June 1999.Rottensteiner, F. and C. Briese. A New Method for Building Extraction in Urban Areas from High-Resolution LIDAR Data. IAPRSIS, Vol. XXXIV/3A, Graz, Austria, pp. 310-317Song, W., and T.L. Haithcoat. Development of Comprehensive Accuracy Assessment Indexes for Building Footprint Extraction. Geoscience and Remote Sensing, IEEE Transactions on. 43:2. February, 2005.