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Asbestos Roof Mapping

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by

Ratih Nurhayati

on 21 May 2015

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Transcript of Asbestos Roof Mapping


Commissioners
: Stefan van Harn
Tom Vollebergh
Team coach
: Ron van Lammeren
July 2014
Team 7:
Joël Davidse
Ratih Nurhayati
Barbara Sienkiewicz
Nikolaos Tziolas
Cornelis ValkTeam number: 7

Team members: Joël Davidse, Ratih Nurhayati, Barbara Sienkiewicz,

Nikolaos Tziolas, Cornelis Valk

Commissioners: Stefan van Harn, Tom Vollebergh

Team coach: Ron van Lammeren

Date: May 2014
ASBESTOS
Results Q4
Two
Discussion & Conclusions

method 1
seems
reliable

method 2

not
(due to inaccurate polygons, method 1 more robust)

method 1
can be used to improve roof area estimate
Debris is small
Results Q1 & Q2
Mapping asbestos-cement rooftops using remote sensing techniques
Problem

background
Discussion & Conclusions
Q1
accuracy is reasonable
-> further improved by more training data

Q2
accuracy not enough
-> might be further improved by refining methods
Results
Results Q3
Type
: not very relevant
State
: age, broken, moist,... quality!
Quality indicator
: number of asbestos fibres surfacing
Procedure valid but vegetation interferes

No clear link quality <-> age, so map not reliable

Hyperspectral only option expensive

More unique characteristics can be found with more expensive instruments (measuring thermal infrared):


no interference of vegetation
ArcGIS vector dataset containing buildings with attributes:
OBJECTIVES

Q1 accuracy

85.6%

Q2 accuracy


65.3% (object-based)
69.4% (unsupervised)




Results Q5
Results Q6
Discussion & Conclusions
Q1
: Can asbestos rooftops be detected by analysing hyperspectral remote sensing data? And how accurate is the result?


Q2
: Can asbestos rooftops be detected by analysing multispectral remote sensing data? And how accurate is the result?
Research Questions 1 & 2
Can a distinction be made concerning the type and state of the asbestos material by analysis of hyperspectral data? And how?
In which way can different data sources and information be integrated, making it available in one system?

Research Question 5:
Can the current estimates of the total surface of asbestos roof-top material be improved?
Research Question 4:
Research Question 3:
What are ideas to make a good inventory of contaminated asbestos areas after a calamity/disaster situation?

Research Question 6:
Methods
Two
Different
Results
Address
Year of construction
Estimated roof area
Asbestos classification
also converted to TEXT FILE
Field survey
Best classification


All methods applied are documented
- Report

And can be reproduced
- ArcGIS Models and scripts
- Technical documentation

Currently:
- Upscaling to national level expensive, but possible

Future:
- Rapid progress in technology
- Satellite may be feasible
- Decrease of costs

Road ahead for Achmea
high resolution required
AC debris looks like concrete debris
fingerprint accurately measured
Prerequisites

Event is unexpected
quick image acquisition
Weather circumstances vary
Platform
Restriction
Lo
cation
Surface Ar
ea
Con
dition
Debris detecti
on after event
Data Inte
gration
Government's goal
asbestos removed by 2024
Problem
Uncertain asbestos usage : 100-150 million m2
Achmea’s interest
Provides services to major share of agricultural sector
Reducing farmers' risks
Assist in transition to asbestos-free
Field survey very expensive
exploring alternative methods
Top view: 46 365 m2
Method 1: 48 946 m2
Method 2: 61 986 m2
UAV + hyperspec
Camera
high cost
CAMERA
Good Accuracy
AC
UISITION
RESOLUTION
dependent
Panchromatic
or
Multispectral
Satellite Hyperspectral
AIrborne
Hyperspectral
UAV Hyperspectral
Team 7:
Joël Davidse
Ratih Nurhayati
Barbara Sienkiewicz
Nikolaos Tziolas
Cornelis Valk
Commissioners:
Stefan van Harn, Tom Vollebergh
Team coach: Ron van Lammeren
Expert : Harm Bartholomeus
July 2014
Questions?
Full transcript