ESTIMATION OFEARTHQUAKE DAMAGEFROM AERIAL IMAGES

BY PROBABILISTIC METHOD

Shota Izaka, Hitoshi Saji

(Shizuoka University)

Introduction

Backgrounds

• After large-scale earthquake– Urban areas are seriously damaged– Many people require rescuing and aid

• For effective rescue and victim support– Rapid action is needed– A wide range of information is important

Aerial images are suitablefor disaster

observation

Conventional method

• Matching analysis– Comparing pre-disaster and post-disaster

images

• Difficulty of matching analysis– Difficult to obtain pre-disaster images– Affected by shooting conditions and time

• Changes of shadows• Construction and destruction of buildings

Our goals

• Rapid analysis of damage– Use only post-disaster aerial images– Not using the training data

• Assisting various rescue and victim support activities– Providing information available for various

purposes

Assisting human decisions

Ways of assisting human decisions

• Remaining undetermined regions– We don’t force to classify all regions– The final decision is left to the people in the

field

• Showing the likelihood of damages– The result available for various purposes

• Target area estimation of rescue activity• Determination of the road passable for

emergency vehicles

Method

Overview

Aerial Image

Segmentation

Featureextraction

Resultfor buildings

Digitalmap

Regionclassification

Resultfor roads

Road mask creation

Road mask creation

• Creating road mask from digital map– Roads change little over time

Our method is not affected by the time

when the map is created

Digital map Road mask

Segmentation

• Initial Segmentation– Segment into small basic regions

• Unification of similar regions– Considering color and textures– Avoiding to unify roads and buildings

Before segmentation After segmentation

Feature extraction

• Collapsed buildings– Segmented into small regions– Having short random edges

Extracting short edgesas a feature of damages

Collapsed buildings Segmented regions Edges

Feature extraction

• Undamaged buildings– Maintaining their shapes– Having a large area

Extracting building regionsas a feature of

undamaged

Undamaged buildings Segmented regions Edges

Region classification

• Using the probabilistic relaxation method– Labeling method using the probability

We use the method to classify each region by damage probability

Defining initial probability

• Considering extracted features– The proportion of short edges– The area of region– Building region or not

Large area

BuildingHigh short edge rate

Probability definitions

Probability update

• Update using similarity– Considering the region similar to damaged

region as damaged region

Probability update model

Low

High

High

High

High

High

High

High

High

High

High

High

High

High

High

High

Extracting undamaged regions

• Regions are converged high or low probability

• Extracting low probability regionsas undamaged

regions– Considering regions not converged

as undetermined regionsHigh

probability

Result of extraction

Low probability

Undetermined

Extracting damaged regions

• Extracting damaged regions

from high probability regions

Highprobability

Damaged regions extraction model

Lowprobability

Undetermined

Damaged

Undetermined

Redefining initial probability

• Redefining probabilityby randomness of edges

– Using variance of edge angles

Edge model ofundamaged buildings

Edge model ofcollapsed buildings

Result of classification

• ■:Undamaged regions• ■:Undetermined regions 1

– Low risk of damage

• ■:Undetermined regions 2– High risk of damage

• ■:Damaged regions

Result of classification

Undetermined

Damaged

Undetermined

Undamaged

Image division

• Dividing a result image into buildings and roads– Result of buildings

• Estimation of building damages

– Result of roads• Determination of road passable

Experiment

Data

• Aerial images– Great Hanshin Earthquake– Captured on January 18, 1995– Provided by PASCO Corp.

• Digital map– A topographic map of Kobe city– Provided by Kobe City Urban Planning

Bureau

Result of classification for buildings

Input image Result image

■:Undamaged regions ■:Undetermined regions 1■:Undetermined regions 2 ■:Damaged regions

Result of classification for roads

Input image Result image

■:Undamaged regions ■:Undetermined regions 1■:Undetermined regions 2 ■:Damaged regions

Evaluation of accuracy

• Creating answer images– Using visual judgment

• Comparing with results

Result of classification

Undetermined

Answer

Damaged Undamaged

DamagedUndamaged

Undetermined

Detection rate

• Evaluating pixels in same category

Result of classification

Answer

Damaged UndamagedDamaged

Undamaged

Damaged

Undamaged DamagedUndamaged

Detection ratewith human decisions

• Estimating rate after human decisions– Adding undetermined regions

Result

Damaged

Undamaged

Answer

Damaged Undamaged

DamagedUndamaged

Damaged

Undamaged

False detection rate

• Evaluating pixels in wrong category– Visual judgment

Considered undamaged regions

Damaged

Undamaged Considered damaged regions

Result of classification

DamagedUndamaged

Answer for buildings

Result imageAnswer image

■:Undamaged regions ■:Undetermined regions 1■:Undetermined regions 2 ■:Damaged regions

Answer for roads

Answer image Result image

■:Undamaged regions ■:Undetermined regions 1■:Undetermined regions 2 ■:Damaged regions

Result of accuracy evaluation in buildings

• Undamaged regions– Detection rate:77.2%

• With human decisions:93.1%

– False detection rate:10.1%

• Damaged regions– Detection rate:74.0%

• With human decisions:87.0%

– False detection rate:17.7%

Result of accuracy evaluation in roads

• Undamaged regions– Detection rate:85.5%

• With human decisions:93.4%

– False detection rate:19.0%

• Damaged regions– Detection rate:65.3%

• With human decisions:79.6%

– False detection rate:14.6%

Review of results

• Obtained high detection rates– Except for damaged regions in roads

• Features of damage on roads are unclear– Many regions classified into “Undetermined”

Requiring human decisions

Road image Result of classification

Review of results

• Obtained low false detection rates– Roads have more errors than buildings

• Caused by objects on roads– Cars, roofs, shadows of buildings

Roof and car Error Shadow and car Error

Conclusion

• Our results can be used for various rescue and victim support activity– Estimation of building damages– Determination of road passable

• Our future directions– Improving building detection– Detecting objects on roads

End

The Sendai earthquake

• Most of the damage was caused by the Tsunami

• Most of the buildings are flooded out– Our method aim to detect collapsed

buildings

• Huge area of damage– Not possible to capture by aerial images

Applying to the earthquake is future works