Dr Mehdi Ravanbakhsh (Landgate & CRCSI),

Rebecca Prior, David Elliott, Paul Duncan, Dr Matthew Adam, Dr Brendon

McAtee (Landgate)

An Eye on Buildings:

Application of Photogrammetric Computer

Vision to the Extraction of Building Footprints

in Urban and Suburban Perth

Contents

Introduction

Model

Strategy

Evaluation

Outlook

CRCSI

Contents

Introduction

Model

Strategy

Evaluation

Outlook

CRCSI

Introduction

Building footprint data is important, especially in emergency service

applications

Landgate (WA) currently have almost 1 million buildings in database

Capture is manually intensive and time consuming

Landgate needed a cost effective, efficient and sustainable process

Research funding provided through Landgate Innovation Program

via CRCSI

CRCSI

Problem

Current approaches

Those reliable rely on LiDAR data

Expensive for use in operational environment

Measurement still relying on human operator

Frequent updating is required (every 6 months)

An operational computer vision method for

delineation of building footprints Solution

CRCSI

Previous Work

[Awarangjeb et al 2010 at

CRCSI]

Initial identification: threshold

LiDAR data

Delineation: Edge detection

and modelling

Slow (13.5 minutes for 132

buildings), rectangle shape

assumption and heavily

relying on LiDAR

CRCSI

Contents

Introduction

Model

Strategy

Evaluation

Outlook

CRCSI

Model

3D data is essential

LiDAR versus multi-view

imagery

Model based versus data-

driven approach

Optimisation methods like

active contours not suitable in

operational setting

CRCSI

Contents

Introduction

Model

Strategy

Evaluation

Outlook

CRCSI

Strategy

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Pre - processing ( DSM

filtering , threshholding nDSM , NDVI masking )

Vector data filtering , morphological analysis

and smoothing

Quality Assessment & Reference Data Generation

Coarse building outline

Final Outline

INPUT DATA RESULT

Multi - view Imagery Building Outline

PROPOSED APPROACH

Cadastral Data

Quality Indices

Contents

Introduction

Model

Strategy

Evaluation

Outlook

CRCSI

Evaluation

CRCSI

Dataset: 2 test datasets

from Perth urban and

suburban areas (657

buildings for test site 1)

RGB & NIR images at 15

cm GSD

Existing cadastre data

available

Industrial and residential

building types

Manual Method

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Automated Method

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Outcome Benefits – Business Application

CRCSI

65% time saving in capture and QA of buildings

Allows capture in areas previously uncaptured due to

resource restraints (eg rural/regional areas)

Increased volume of location information accessible to the

community

Allows greater frequency of capture – annual vs five-yearly

Allows resources to focus manual effort on built up areas

with tall buildings and requirements for 3D e.g. CBD

More cost efficient process

No requirement for LiDAR

Uses existing imagery and data captured by business for

other purposes

Greater Update Frequency

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Better data currency due to reduced processing time

Manual Capture (cyan) Automated Capture (red)

Residential

Greater Update Frequency

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Better data currency due to reduced processing time

Manual (cyan) vs automated (red)

Residential

Greater Update Frequency

CRCSI

Better data currency due to reduced processing time

Manual (cyan) vs automated (red)

Residential

Greater Update Frequency

CRCSI

Better data currency due to reduced processing time

Manual (cyan) vs automated (red)

Industrial

Quality Indices

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Test site 1 Test site 2

Total 657 buildings

Average building

coverage 73%

Total 6,276 buildings

Average building

coverage 71%

Average capture quality lower than manual method

Output is 2D compared to 3D from manual method

2D data is sufficient for many operational purposes

Areas of further research

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Jagged line in oblique building footprints

Coverage still at acceptable levels

Areas of further research

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Occlusion caused by trees and shadows

Areas of further research

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Voids in DSM at large industrial buildings (image matching issue)

Research comparison

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Rectangle shape assumption and output from previous

research

Research comparison

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Outputs from current research

No assumption on shape

Better representation of actual

building outline

Research comparison

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Ultra-fast approach: 15 seconds computation time for 657

buildings

Single source Data: Versus integrated LiDAR and imagery

Complete and correct results

Result of the previous research Result of the current research

Contents

Introduction

Model

Strategy

Evaluation

Outlook

CRCSI

Outlook

CRCSI

Build on positive outcomes of this research

More research into correcting occlusion from trees

and shadows

More research into capture of buildings that do not

align with imagery pixels

Research into use of automated process for change

detection

Integrate research outcomes into business processes

Thank you

CRCSI