Pollino M., Cappucci S., Giordano L., Iantosca D., De Cecco L., BersanD., Rosato V., Borfecchia F.

ASSESSING EARTHQUAKE-INDUCED URBAN

RUBBLES BY MEANS OF MULTIPLATFORM

REMOTELY SENSED DATA

AIT- Bologna, Italy June 25 - 26, 2019

12° Workshop Tematico di Telerilevamento

Bologna 25-26 Giugno 2019

Keywords: Seismic Post-emergency Management, active (LIDAR)/passive remote sensing, Sentinel2 & World View satellites, COPERNICUS, hyperspectral signatures

of urban rubble materials, SMA (Spectral Mixture Analysis), Classification & machine learning algorithms

Summary• The huge amount of rubble from partial or total collapse of buildings/structures, caused

by an earthquake hitting densely urbanized areas with vulnerable historical centers,

must be mapped and characterized in order to suitably plan the typical emergency

activities for ensuring the accessibility, rescue and first assistance, …

• In the post-emergency, reliable information about the distribution and characterization

in term of volume/weight and typology of seismic rubble are fundamental for their

proper management with handling, accumulation and transportation to final disposal to

predisposed storages or reprocessing

• Despite this information is particularly valuable for optimizing the emergence/post -

emergency responses, there are not many methods to provide extensive and reliable

estimates of the amount of the seismic urban rubbles in terms of volume/weight and

typologies

• Trying to satisfy these needs, an integrated methodology for assessing the volume

and typologies of rubble heaps in a real seismic emergency/post emergency scenario,

based on currently available earth observation (EO) active (LIDAR) and passive

(multi/hyperspectral) data, has been designed and developed using Geomatics

techniques. Its preliminary results are presented and discussed here

HIGHLIGHTS

EMS RAPID MAPPING AND AERIAL SURVEYS satellite & aerial acquisition in the aftermatch of the 2016 earthquake

Copernicus EMS service activation

Sentinel 2, aerophotos &LIDAR

Area of interestin the historical

center of Amatrice

Copernicus EMS building damage grading

THE RED AREAS OF AMATRICE

12° Workshop Tematico di Telerilevamento

Bologna 25-26 Giugno 2019

S. Agostino Church of Amatrice

Seismic urban rubble delimiting the main street of Amatrice

The most damaged areas of the historical center of Amatrice

THE METHODOLOGY SCHEMA Representative rubble piles of red areas were exploited for implementation

Heaps contours &

Volumes

Preprocessing

of satellite frames

Atmospheric correction

Orthocorrection

Resampling

Rubble Tipology

percentages of piles

In situ

Photo-interpretation

of rubble

Aerophoto (Individuation & Contours)

SMACC – SMA

on piles areas

Endmembers (EM) finding

Percentage of EMs on heaps

Machine learning

classification

of resampled field

to EMs signatures

SVM ; ANN ; RnTr

OUTPUT 1

Spectral Mixture Analysis & per

pixel EMs percentages, Soft

classification

3d GIS points cloud modelling

Endmembers

mapping to

resampled

in situ

spectral

signatures

OUTPUT 2

Field point hyperspectral signatures

of building materials (23 FSP)Acquisistion

Preprocessing

Spectral resampling GIS processing

of outputsEndmembers (EMs)

FSP Recode & Zonal mapping

Estimate

of Piles Volume (m3)

LIDAR data selection

Surfaces discrimination & fitting

EMS Copernicus

1

3

2

INPUT:1. LIDAR points cloud data, Aerophotos, EMS

damage grading maps2. Sentinel 2 , WorldView 3 (WV3), 8 bands

multispectral data3. ASD FieldSpecPro hyperspectral signatures

Rubble heaps geometry Rubble heaps typology

RUBBLE HEAPS GEOMETRY

Distribution, delimitation and volume assessment

Preprocessing and integration of different information layers into a Web-GIS infrastructure:

1. 1 m-pixel Digital Terrain Model (DTM) and Digital Surface Model (DSM); derived from LIDAR data acquired during 2016 emergency flight of National Civil Protection Department CPD;

2. RGB orthophotos (15 cm ground spatial resolution, 2016 emergency flight);

3. 1 m-pixel DTM and DSM derived from 2008-2012 pre-event LIDAR data provided by flights of Italian Ministry of Environment

4. Damaged/collapsed buildings: delimitation / classification provided by Copernicus Emergency Management Service EMS).

Simplified conceptual schema for rubbleheaps delimitation and volumes assessment

RGB Aerophotos

EMS buildings damagegrading

PHOTOINTERPRETATION

hth heapdelimitation

DTM2016

Sh=base surface

DSM

DTM

Volh[m3] = σ𝑘=0

𝑛ℎ (DSM k − DTM k )𝑑𝑠

DTM2008-2012

VOLUMES ESTIMATE

Selection of the best DTM in case of uncertainties due to sparse rubble

Sum over kth pixel extended to all nh pixels of Sh assessed for hth heap

Using DSM and DTM obtained from LIDAR points cloud

a c

b d

RUBBLE HEAPS GEOMETRY Distribution, delimitation and volume assessment

Semi-automatic procedure for rubble heapsdelimitation and volumes assessment

Thematic products for rubble heaps of Amatrice center

HYPERSPECTRAL SIGNATURES Field acquisition and pre-processing of hyperspectral signatures

Urban rubble materials typologies identified on field.

N Material

1 rubble debris

2 cement

3 tiles

4 bricks

5 natural stones

6 zinc gutter

7 bitumen sheath

8 copper gutter

Hyperspectral signatures acquired through ASD FieldSpec Pro hand hedradiometer for rubbe debris and cement typologies (respectively material

1 and 2 of table above) identified on field.

To preserve the spectral variability ofthe different building materials foundon different sites of field campaign, foreach of the first five typologies fourrepresentative hyperspectral signatureswere preprocessed and stored in thelibrary with the three remaining, for atotal of 23 signatures.

The field campaign acquisition of the hyperspectral signatures of building materials composing the urban rubble has been carried out in three different sites of the Amatrice red zones during 6 and 7 December 2016. Here the sites have been reported with red dashed contours with the ruble heaps previously found (blue shadow) over a World View 3 true color image (1.35 m a.g.r.).

Hyperspectral signatures acquisition through ASD FieldSpecPro hand held radiometer

8 bands WorldView3 (1.35 a.g.r.) frame of 25-08-2016

RUBBLE HEAPS TYPOLOGY Hyperspectral signatures resampling, SMACC EMmembers & Soft Classification

a

N Material

1 rubble debris

2 cement

3 tiles

4 bricks

5 natural stones

6 zinc gutter

7 bitumen sheath

8 copper gutter

field rubble typologies

Library of 23 hyperspectral signatures

23 resampled hyperspectral signaturesto 8 WorldView-3 spectral bands

SMACC 1(Sequential Maximum Angle Convex Cone) algorithm finds spectral endmembers (EMs) as extreme independent vectors within a data set that can be used for modelling the others by means of their positive linear combination. The EMs form a convex cone that contains the remaining data vectors and provide coefficients (relative abundances) for their mixture modelling analysis (SMA) .

1) John H. Gruninger, Anthony J. Ratkowski, and Michael L. Hoke "The sequential maximum angle convex cone (SMACC) EMmember model", Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and UltraspectralImagery X, (12 August 2004); https://doi.org/10.1117/12.543794

EM Typolgy Discr. Fun.

EM1 2-cem 0.199976

EM2 5-nat. stone 0.160118

EM3 4-bricks 0.205488

EM4 5-nat. stone 0.226696

EM5 4-bricks 0.262556

EM6 5-nat. stone 0.181925

EM7 2-cem 0.19679

EM8 3-tiles 0.161458

EM9 2-cem 0.201554

SVM(Support Vector Machine) – BALLSUPERVISED CLASSIFICATION

EMs spectra signatures (left graph), true color WV3 image of rubbleheaps (upper left) and false color of EMs relative SMA abundances (upperright image & table beneath)

TRAINING

RESAMPLINGUsing WV3 bands filterfunctions INPUT

RESULTS & VALIDATIONRubble heaps distribution, volume and typologies

Rubble heaps delimitation and volumes assessment Surface rubble typologies % for rubble heaps of Amatrice center

Polygonal distribution of rubble heaps and related volumes (Table on the right) derived from the orthophotos (background) and LIDAR data. The Copernicus EMS building damage grading is displayed as background

Estimated percentage distributions of the types of rubble heaps found in the center of Amatrice town. The four maps refer respectively to: cement-rubble (lower left), natural bricks (lower right), bricks (upper left) and other building materials (upper right).

id_pilevolume(m3)

concreteanddebris(%)

bricksandtiles(%)

naturalstones(%)

othersmaterials (%)

hps1 2478.63 54 28 10 8hps2 1494.47 50 28 12 10hps3 3471.36 56 31 6 7hps4 4199.4 57 27 10 6hps5 1490.97 54 29 10 7hps6 2010.39 52 29 10 9hps7 865.79 53 30 8 9hps8 876.687 56 29 8 7hps9 5118.51 54 29 8 8

hps10 7140.05 56 30 7 7hps11 1314.63 49 27 10 14hps12 1228.15 55 30 8 7hps13 4612.94 55 29 8 8hps14 1221.36 57 31 7 5

Difficulties : spectral confusion

between •rubble debris – cement•bricks – tiles (reddish)

surface typologies

Given the general lack of specific independent information, preliminary verification and validation of results have been attempted using:• the RGB aerophotos (25 cm a.g.r) with photointerpretation and usual

thematic classification approaches;• fragmentary information from the subsequent rubble transportation

activities to final disposal ;with a general agreement within 75-80% found

CONCLUSIONS & PERSPECTIVESEO based applications for supporting emergency/post emergency management

These preliminary EO based results, for seismic urban rubble assessment, show a generalagreement with the available information, even if they must be further deepened andvalidated using more suitable independent data

They may provide thematic information complementary and synergic with those alreadymade available by Copernicus EMS that strongly support effective emergency responses ofhit territories

The optimized management of seismic rubble can be suitably supported by EO basedmonitoring and mapping applications including the last generation of themulti/hyperspectral satellite HR/VHR sensors

In the framework of national/EU monitoring plans, preventive LIDAR acquisition should beregularly carried out in order to cover the entire earthquake prone territory at Europeanlevel

In the improvement perspective of supporting to the emergency management, the fullexploitation of potentiality and synergy of currently available EO multiplatform data, interms of their multi/hyper spectral capability, geometric resolution (i.e. panchromaticchannel) and stereo acquisition, should be pursued