ESTIMATION OF EARTHQUAKE DAMAGE FROM 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 suitable for 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 Feature extraction Result for buildings Digital map Region classification Result for roads 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 mapRoad mask

Segmentation Initial Segmentation –Segment into small basic regions Unification of similar regions –Considering color and textures –Avoiding to unify roads and buildings Before segmentationAfter segmentation

Feature extraction Collapsed buildings –Segmented into small regions –Having short random edges Extracting short edges as a feature of damages Collapsed buildingsSegmented regionsEdges

Feature extraction Undamaged buildings –Maintaining their shapes –Having a large area Extracting building regions as a feature of undamaged Undamaged buildingsSegmented 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 Building High 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

Extracting undamaged regions Regions are converged high or low probability Extracting low probability regions as undamaged regions –Considering regions not converged as undetermined regions High probability Result of extraction Low probability Undetermined

Extracting damaged regions from high probability regions High probability Damaged regions extraction model Low probability Undetermined Damaged Undetermined

Redefining initial probability Redefining probability by randomness of edges –Using variance of edge angles Edge model of undamaged buildings Edge model of collapsed 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 imageResult image ■:Undamaged regions■:Undetermined regions 1 ■:Undetermined regions 2■:Damaged regions

Result of classification for roads Input imageResult 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 DamagedUndamaged Damaged Undamaged Undetermined

Detection rate Evaluating pixels in same category Result of classification Answer DamagedUndamaged Damaged Undamaged Damaged Undamaged Damaged Undamaged

Detection rate with human decisions Estimating rate after human decisions –Adding undetermined regions Result Damaged Undamaged Answer DamagedUndamaged Damaged Undamaged Damaged Undamaged

False detection rate Evaluating pixels in wrong category –Visual judgment Considered undamaged regions Damaged Undamaged Considered damaged regions Result of classification Damaged Undamaged

Answer for buildings Result imageAnswer image ■:Undamaged regions■:Undetermined regions 1 ■:Undetermined regions 2■:Damaged regions

Answer for roads Answer imageResult 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 imageResult 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 carError Shadow and carError

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