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E. Rupnik – HIGH RESOLUTION AIRBORNE IMAGES FOR EARTHQUAKE DAMAGE ASSESSMENT

Automated processing of high resolution airborne images for earthquake

damage assessment

Ewelina RUPNIK

3D Optical Metrology (3DOM)

Bruno Kessler Foundation (FBK)

Trento, Italy

Email: [email protected]

http://3dom.fbk.eu

E. Rupnik – HIGH RESOLUTION AIRBORNE IMAGES FOR EARTHQUAKE DAMAGE ASSESSMENT 2

OUTLINE

Introduction

Airborne imaging platforms

Photogrammetric processing

Earthquake damage assessment

Methods

First results

Conclusion and future work


INTRODUCTION

Optical imagery for earthquake damage assessment

Different temporal and spatial resolutions (airborne, spaceborne)

Pre- and/or post-event data

Use of ancillary data, fusion (LiDAR, maps, GIS)

2D and/or 3D approaches

Nadir and/or oblique imagery


INTRODUCTION

Airborne imaging platforms for earthquake damage assessment

Traditional aerial flights e.g. [Li et al., 2014; Gerke and Kerle, 2011;]

San Francisco Earthquake, 1906 – images from kite-borne camera

Wenchuan earthquake 2010, China – nadir views

Haiti earthquake 2010, Dominican Rep. – nadir and oblique views

UAV e.g. [Chou et al., 2010; Huber 2010; Adams and Friedland, 2011]

Hurricane Katrina 2006, Mississipi Gulf, USA

L’Aquila earthquake 2010, Italy

Haiti earthquake 2010, Dominican Rep.

Japan earthquakes 2011


PHOTO-INTERPRETATION apt for non-experts

COMPLETENESS 2.5 versus 3D

COST & ACESSIBILITY systematic acquisition, growing market:

Pictometry, MIDAS, DigiCAM, UltraCAM Osprey,

Leica RCD30

Airborne imaging platforms for earthquake damage assessment

Traditional aerial flights e.g. [Li et al., 2014; Gerke and Kerle, 2011;]

San Francisco Earthquake, 1906 – images from kite-borne camera

Wenchuan earthquake 2010, China – nadir views

Haiti earthquake 2010, Dominican Rep. – nadir and oblique views

UAV e.g. [Chou et al., 2010; Huber 2010; Adams and Friedland, 2011]

Hurricane Katrina 2006, Mississipi Gulf, USA

L’Aquila earthquake 2010, Italy

Haiti earthquake 2010, Dominican Rep.

Japan earthquakes 2011

5

INTRODUCTION


OBJECTIVE

Earthquake–induced building damage

area, grade of damage, type of damage

European Macroseismic Scale 1998 (EMS98)

Grade 1 Grade 2 Grade 3 Grade 4 Grade 5


OBJECTIVE




Grade 3 – 5 (EMS98)



OBJECTIVE





Individual building,

Roof damage, and

Façade structural damage detection

Building Damage Assessment Map



OBJECTIVE





Individual building,

Roof damage, and

Façade structural damage detection

Building Damage Assessment Map


Evaluate with:

Spectral information in nadir and oblique imagery, and

Geometric information in photogrammetric DSM



Input imagery

Diff. resolution

Diff. imaging geometry

Manned/unmanned, nadir/oblique

Different resolution

Different imaging geometry

Consequences/challenges

Varying scale within images

Less similarity between images

Occlusions

Complex image overlaps

AUTOMATED PROCESSING MORE DIFFICULT



Image orientation [Rupnik et al., 2013]

Tie point extraction

Approximate orientation

Direct methods

Orientation built step by step

Careful concatenation necessary

Refined bundle adjustment

Image dense matching [Nex and Remondino, 2012]

Dense but 1 object point per pixel

Noisy Shadows and occlusion

True orthophoto generation

NA

DIR

O

BL

IQU

E


Given

Spectral → Geometric information

Photogrammetric DSM

True orthophoto

Goal

Thematic information

Ground

Vegetation

Building

Roof

Facade

12

Earthquake damage assessment – methods

Classification 1/3

implicit

explicit



Classification 1/3




Classification 2/3

Sequential approach

15


BUILDING LOCALIZATION

DAMAGE LOCALIZATION

NADIR NADIR

FACADE DAMAGE ASSESSMENT (TO DO)

OBLIQUE


BUILDING

VEGETATION HIGH

VEGETATION LOW

GROUND

Classification 2/3

Sequential approach

16



NADIR


BUILDING INTACT

BUILDING DAMAGED

VEGETATION HIGH

VEGETATION LOW

GROUND

Classification 2/3

Sequential approach

17



DAMAGE LOCALIZATION

NADIR NADIR

XZ = DSM – DEM XSV = SLOPE VARIABILITY OVER A CONSTRAINED REGION FEATURES X


verification of harmed entities structural damage assessment by

pointcloud modelling and 2D image processing

Classification 2/3

Sequential approach

18



DAMAGE LOCALIZATION

NADIR NADIR

FACADE DAMAGE ASSESSMENT (TO DO)

OBLIQUE


Classification 3/3

Dataset

Where – San Felice, Italy

When – May 2012

Magnitude – 6.0 MMS

Nadir post – UltraCAM XP, ~5cm GSD, 80/60 overlap

Oblique post – Midas 5, ~10cm GSD (nadir), 70/50 overlap

19

Earthquake damage assessment – results


Classification 3/3

20


B DAMAGED B INTACT

VEGETATION GROUND

Area 1


Classification 3/3

21


B DAMAGED B INTACT

VEGETATION GROUND

Area 2


A methodology was presented that uses

- spectral and

- geometric information derived from optical images

in order to classify buildings as intact or damaged in an unsupervised manner;

Adopted features (PNDVI, Z, SLOPE VARIABILITY) with their normaliztion factors are discriminative yet occasionally might prove insufficient thus more feature should be tested and added to the model (possibly through learning);

EMS Grade 4 -5 are detectable with the given approach

Further novelty of the approach is in the unsupervised approach and the use of photogrammetric DSM;

Despite the developments in digital photogrammetry, the DSM produced in an automated manner can be noisy because of occlusion and shadowing effects;

Appropriate filtering techniques can largely mitigate the blunders in DSM

Future works include the use of oblique images and the derived pointclouds to verify the potentially harmed entities as well as evaluate EMS Grade 3 damages

22

Conclusions and future work


Bibliography

Adams, Stuart M., and Carol J. Friedland, 2011. A survey of unmanned aerial vehicle (UAV) usage for imagery collection in disaster research and management. 9th International Workshop on Remote Sensing for Disaster Response.

Boykov, Y., Veksler, O., Zabih, R., 2001. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (11), 1222–1239.

Chou, T.-Y., M.-L. Yeh, et al., 2010. Disaster Monitoring and Management by the Unmanned Aerial Vehicle Technology. ISPRS Technical Commission VII Symposium. W. Wagner and B. Szekely. Vienna, Austria, IAPRS. XXXVIII: 6.

Dong, L., and Shan J., 2013. A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS Journal of Photogrammetry and Remote Sensing 84, 85-99.

Gerke, M., Kerle, N., 2011. Automatic structural seismic damage assessment with airborne oblique pictometry imagery. Photogrammetric Engineering and Remote Sensing 77 (9), 885–898.

Huber, M. 2010. Evergreen supports UAV team mapping Haitian Relief. Aviation International News. March 2010.

Li, Z., Jiao, Q., Liu, L., Tang, H., & Liu, T., 2014. Monitoring Geologic Hazards and Vegetation Recovery in the Wenchuan Earthquake Region Using Aerial Photography. ISPRS International Journal of Geo-Information 3.1, 368-390.

Nex, F., Remondino, F., 2012: Automatic roof outlines reconstruction from photogrammetric DSM. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. I(3), 257-262.

Rupnik, E., Nex, F., and Remondino, F., 2013. Automatic orientation of large blocks of oblique images. Int. Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 40(1/W1), 299-304

Documents

Automated processing of high resolution airborne images ...rapidmap.fbk.eu/sites/rapidmap.fbk.eu/files/Rapidmap_Tokyo_Rupnik.pdf · 3 E. Rupnik – HIGH RESOLUTION AIRBORNE IMAGES