ESTIMATION OF EARTHQUAKE DAMAGE FROM AERIAL IMAGES BY PROBABILISTIC METHOD Shota Izaka, Hitoshi Saji...
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Transcript of ESTIMATION OF EARTHQUAKE DAMAGE FROM AERIAL IMAGES BY PROBABILISTIC METHOD Shota Izaka, Hitoshi Saji...
ESTIMATION OFEARTHQUAKE DAMAGEFROM AERIAL IMAGES
BY PROBABILISTIC METHOD
Shota Izaka, Hitoshi Saji
(Shizuoka University)
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
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
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