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Open Access Discussions erent from earth- ff Sciences , and X. Tong 1 Natural Hazards Hazards Natural and Earth System System and Earth , C. Jiang 1 , H. Wu 1 7138 7137 , X. Jie 1 cient rescue operation. ffi , S. Ma 3 , Q. Fu 2,* , Z. Hong 1,* This discussion paper is/has beenSystem under Sciences review (NHESS). for Please the refer journal to Natural the Hazards corresponding and final Earth paper in NHESS if available. Department of Disaster and Emergency Medicine, Eastern Hospital, College of Surveying and Geo-informatics, Tongji University, Siping road No. 1239, Research Institute of Structural Engineering and Disaster Reduction, College of Civil These authors contributed equally to this work. Building damage is the main contributorsuch to as earthquake Japan casualty (Yamazaki except et some al., countries, 1996). Compared to developed countries, buildings hit coming more necessary asand the city dramatically world (Wyss population andcaused increases Trendafiloski, 2011). and great In recent concentrates damage years, in have earthquakesWenchuan occurred town that earthquake frequently (der all Hilst, over2009), 2008), the the 2010 the world, Chile 2009 such earthquakeet as L’Aquila al., (Lay earthquake the 2013), et the (Ameri 2008 al., 2010 et 2010),quake Yushu earthquake the (Mimura al., (Ni et 2010 et al., Hati al., earthquak 2011) 2010),In the and this (Daniell 2011 situation, the Japan the 2013 Great prevention Ya’an earth- earthquake and relief (Tang and of Zhang, earthquake 2013). drew a great deal of attention. information to help develop an e 1 Introduction Estimating human losses due to earthquakes in real-time and scenario mode is be- This advantage provides the possibilityately to after earthquake. estimate With the respect earthquake tofor capability casualties of estimating immedi- HRSI, the this paper casualty buildsand a number new damage in model an of earthquake buildings.2008 disaster Three Wenchuan based earthquake groups on and of theproposed 2010 earthquake model. attributes Yushu The data, earthquake, estimating results were 2003 indicateracy used that Bam significantly. the to Meanwhile, earthquake, model evaluate the has our improved parametersquake the in accu- between model developed should and be developing di countries. This study can provide valuable An accurate estimation of theaster and number increase of the casualties number canjor of help cause survivors. respond Building of damage earthquake earthquake is dis- satellite casualties considered imagery in as (HRSI) the many can ma- developing be countries. used to The detect high-resolution the damage of buildings in a short time. Abstract Nat. Hazards Earth Syst. Sci.www.nat-hazards-earth-syst-sci-discuss.net/1/7137/2013/ Discuss., 1, 7137–7166, 2013 doi:10.5194/nhessd-1-7137-2013 © Author(s) 2013. CC Attribution 3.0 License. 1 Tongji University School of2 Medicine, Jimo Road No. 150, ShanghaiShanghai 200120, 200092, China China 3 Engineering, Tongji University, Siping* road No. 1239, Shanghai 200092, China Received: 7 November 2013 – Accepted: 22Correspondence November to: 2013 C. – Jiang Published: ([email protected]) 5 and December X. 2013 Published Tong by ([email protected]) Copernicus Publications on behalf of the European Geosciences Union. Application and prospect of high-resolution remote sensing and geo-information system in estimating earthquake casualties T. Feng 5 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | cient rescue plan ffi erences, Aghamoham- ff erent countries can also be achieved ff 7140 7139 ected the casualty number and then proposed an ff ecting the number of casualties. The final number of casualties could be calculated As the development of society, people-oriented research will be paid more attention. Previous researches can contribute much to the prevention of earthquake, but new Among all rescue activities, casualty estimation is the primary information to support ff valuable to know the change of survivor number in the scene. Therefore, the change landscape and access the2011; risk Ehrlich or et al., theto 2010; loss improve Dekker, caused the 2011). by quality Resultsa earthquake of of casualty (Dell’Acqua remote the estimation et sensing livingmethods, model should al., of be the based human used on advantages being.casualty remote of For rather sensing. the this than Compared proposed reason, simpleor machine to this model fitting learning paper other was method method built existing built (Feng (Aghamohammadia et et from al., deep al., the 2013). 2013) analysis mechanism Besideswith of a of quantitative casualty potential evidence. high mechanism Based accuracy on in of our di estimation, experience on the scene of earthquake, it is 2013; Lu et al., 2013; Tong etlower al., prices, 2013; short Huang et revisit al., time, 2013)Tack adaptable et owning capability to al., of its 2012). stereo large coverage, imaging (Tao et al., 2004; However, most researches of remote sensing just focused on the change of geological relating with the casualty were alsoand discussed (Gutiérrez Trendafiloski, 2011). et al., 2005; Petal, 2011; Wyss technique must be introduced to helpdeaths, the buildings relief of do earthquake. (Petal, Earthquakesimmediately 2011). don’t after cause If earthquake, we the canbe accuracy know improved of the much. casualty Recent damage estimation studies conditionto in have of detect a used buildings the high-resolution short height satellite time imagery change can of (HRSI) one building after earthquake in a region (Teeuw et al., He used information ofand local then shaking estimated intensity thewas to casualties built calculated (Jaiswal (Wyss, the et 2004). damage al.,madi Based 2011). et of on Because al. buildings this of startedmodel theory, the to (Aghamohammadi a geographic use et di framework the al., machine-learning 2013). method Besides, to the build direct casualties methods estimation and some factors Century. Real-time prediction of earthquake casualties was discussed by Max Wyss. relief. As the information techniques were widely used in the early years of the 21st from the earthquakethe intensity, the year, the time influence whenCoburn of the not any only earthquake warning emphasized occurs,(Coburn, and the 1994). the local Shiono same season built habits factors a of (Tiedemann, functional butrate. relationship Several 1989). also between earthquakes Study collapse make were rate a of discussed andtional in fatality preliminary relationships his of statistics study each and earthquake were reportedwas not the not same casualty a (Shiono, 1995). func- common Therefore, model there tion, to predicting estimate earthquake earthquake casualties casualties.by Due only earthquake to could at technical be restric- that used time. to These assess results the did loss not caused show obvious help for earthquake to estimate casualties. In theiroccurs, study, they population thought building distribution type, timeestimating a when model, earthquake but no1977). In real 1989, case Tiedemann emphasized wasa the discussed quality of (Anagnostopoulos building was and a Whitman, very critical factor ens the situation further.which Therefore, needs in the support the from presentnumber all stage, source of (Brunner a survivors et high after al.,et e 2009) an al., can earthquake 2012). largely that increase the happen in developing countriesthe (Zhang design of rescuepeople plan. can be The saved. less Many methodologies timecasualties were is in developed to used the estimate to the past. earthquake prepare In for 1977, the Anagnostopoulos rescue, and the Whitman more suggested a method sualties were caused by buildingof damage buildings in in developing developed regions. countries Thisings were in is designed 1994 because to Northridge most withstand earthquakenumber quakes, (Peek-Asa et such of al., as 1998), buildings build- while inmainly there due are developing to still countries the a lack great were of funds not (Kenny, 2012). designed The lack to of earthquake seismic knowledge standard wors- by an earthquake in developing countries are damaged more seriously, thus more ca- 5 5 25 15 20 10 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 7142 7141 erences (Turker and Cetinkaya, 2005), to evaluate the damage grade. The ff ected area were collected. In idea situation, the DI (Damage Index) of one ff erence was a new method and had a high accuracy. At the first step, the DEM ff The remainder of this paper is organized as follows. In Sect. 2, we describe our more than 60 % and less than 80 %, the building belongs to D4. The DI were calculated of pre-earthquake was generatedthe by digital points topographic cloud map, (Ma,high 2005) if enough which the to were resolution generatepre-earthquake marked the was of on generated digital images by 3-D evaluation beforeDEM HRSI model earthquake of (Feng (DEM). post-earthquake et were must al., Otherwise be not 2013;topographic the generated Tong et by map DEM al., 3-D of 2012). of HRSI, The post-earthquake. fora there From won’t same the be DEM the height between digital was change pre- calculated. of and points If post-earthquake, belonging the80 the %, to decrease the damage of building grade belongs nearly of to one all D5. building points If of the decease one of building some were points more of one than building were visual interpretation is a directlyfrom method 2-D to remote assess sensing the images,automatic damaged It, classification condition though of based has buildings high onutilizes accuracy, a the needs variety more spectral of time. information band The toolsDEM and to di assess textural the feature damaged condition of of buildings buildings. The veloping countries. Factors that cause thebuildings casualties are in other various classification very ofdamaged. damaged much, This situation even occurred some moreepidemiological casualties in statistics, developed were the counties. regularity not Without of caused ato casualties very sum from by detailed up. D0 building Therefore, to this D3 studymethod buildings only was mentioned focused hard on in the TongCasciati, D4 et and 1998; D5 al. Saito buildings (2012), and etand used including al., DEM the visual di 2004), automatic Interpretation classification (Gamba (Turker and and Sumer, 2008) increased, from 0.2 togroups rather 0.55, than if five group. wegrade D5 less clustered and than damage D4 D4 belong are conditionguished to grouped. using of two The HRSI. groups, building three In respectively. kinds into Damage D4 the of three and view damage D5, condition of are could casualty the be estimation, major distin- damaged determinants buildings of belong injury to and mortality in an earthquake in de- et al. (2005) addressed this issue. The Kappa value between field survey and HRSI field survey. Does the classificationsensing? agree The with three the reports damage of grade Wang detected et by al. remote (2013), Hisada et al. (2005), and Yamazaki model, 3 sets ofnext data sections. of earthquake were used. The whole2.1 process was described Methods in 2.1.1 Damage detection The damage level ofBarbat one et building al., can 2008), be shown classified in into Table 6 1. groups However, the (Yano et classification al., was 2004; based on the alternative methods, such asmatic visual (Benz interpretation (Shalaby et and al.,of Tateishi, 2004) buildings 2007), including were auto- materials appropriateand and methods. used structure In to were calculate the collected thepart, second from MSI a part, local (Materials casualty GIS and attributions estimation Structure database model based Index) on of MSI buildings. and In DI the was third proposed. To evaluate the is made 2 Methods and data This model wasing constituted the by a 3building parts was (Fig. calculated by 1). thea In high earthquake. the change Due of to first the the part, pixel reasons on The such a as HRSIs building the cover- before resolution and of after images and so on. Other data were used tothat evaluate arose our during model. the The process detailed were illuminated steps in of the modelingmodel Sect. carefully and 2. and problems propose a frameworkated of and casualty discussed estimation. with The real model earthquake was data evalu- in Sect. 3. Finally, in Sect. 4 a conclusion was also discussed in this study based our proposed model. Three sets of earthquake 5 5 25 15 20 10 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | C (3) (2) (5) (1) − , where ) 1 t ered from ; and 1 − ff t 2 ( t r − erent for each to e ff ) er from building 1 was the value of 1 ff t t is the scale factor i ( s r hc N were di = r ) t ( ) at which people felt the s 0 t N and v erent material buildings, when ff is the dependent variable of the v C is the available time to escape and − t 1 = ) 1 t − 7144 7143 2 t is from the time ( ( r ) at which the shake stopped. t 1 − t e · ) (4) 1 is the change rate of survivals from  t ) ected by the quantity of the people in the stream. The 1 is the parameter relating to DI and other factors except ), the MSI was shown as the followings, 1 − ff t 3 ), we can derive vt − 2 D 2 4 t N t ( − ( ), r r 1 − N t − ( e ) and (  s  · 2 N is the final casualty rate. 1 D C vt ected by features of the structure, such as the number of stairs, = ff N − ) 1 t N − 2 i  i t ) was the living rate of people who were still trapped in the damaged ( h 1 r hc was a r n − vt Ln 1 is the number of still alive people in damaged building, s ) N was the pre-event height of a point on a building and n v − 1 is the velocity of the people stream, = is the time at which the possibility of survival is nearly none. Usually, the time P ). i s N = i vt N . 1 2 − v N h − N t − N ) was the initial number of trapped people who were still alive. N 1 = vt max Ln( 1 t s C e = is the usual number of people in the building. ( = t By combining Eqs. ( In general, 40%–60% trapped people in collapsed buildings died at once. The num- After the special time passed, people who were still in the building su · s N d e d may be still aliveEq. (3) in as wooden following, frame buildings. The principle can be described in the as ber of death tends towards stabilityunder after one 72 h wooden (Yu et materialsburied al., building under 2013). can People adobe who gain masonry were some buried or brick more masonry survival buildings space so than that ones more trapped people they collapsed, were displayed in Fig. 2 (Feng et al., 2013). We referred the death rate by Eq. ( kind of buildings. Using Eq. ( D and changes along with the buildingN materials. Therefore, Ln(MSI) where is 72 h. One kind of buildings has its own special MSI. where ( buildings, equating to structure and materials, is the survival rate. where building damage, mainly theple falling while objects. big Small onestrapped could falling people trap objects survive people, might was even2011) only caused whether in hit death. there the peo- The was damaged key still building. factor survival that The space helped death (Macintyre rate et of al., di function. corridor width, stairinterval width, of pedestrians the inshake independent the of variable an corridor earthquake and to the stairs time bias ( strength. The can increase velocity ofdamage. the The stream velocity is and alsoescaping exposure a rate less is people as to following su r where N 2.1.2 Building attributes In our previous work (Fengbuildings et have al., high 2013), relationship wethe thought with the quake, earthquake structure they casualty. and When started the people running materials in for of rooms the felt exits and grew to a stream. A good structure where a point after earthquake. DI 5 5 20 25 10 15 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (7) into 2 ected by 7.9. The ) directly. sDI ff 5 1 2 = = M C 0. Based on our in Eq. ( = D ) 7.1. The epicenter of 0 = (DI M C , for 2 E at a focal depth of 14 km (Chen 0 sDI 1 2 36 ◦ = C E near the city of Bam, 180 km southeast N, 96 0 0 7145 7146 17 E and the focal depth was 19 km (Stone, 2008). 12 ◦ 0 ◦ 22 ected cities, Dujiangyan city, was chosen as the re- ◦ ff ected seriously. Jiegu town was involved in our study. In N and 58. ff 0 6 ◦ N and 103. c 0 + 11 is the scale factor. Then ) and (6), we proposed a casualty-predicting model as the follow- ◦ 5 s . (6) Ln(MSI) ) b s + Ln( + 6.6 happened at 05:27 LT on 26 December 2003. Its epicenter whose depth , where Ln(DI) (DI) a s = 1 2 · e cial documents reported that approximately 15 million people were a ected area of Jiegu, 1942 people died and 8283 injured. The detailed casualty M − ffi DI 2Ln ff 1 e = = = C d dDI ing the data into the expression that accorded with the criterion of the model, solving described in Chen etwere al. displayed (2011). in The the remote work of sensing Dou images et that al. covered (2012). Jiegu town 3 Results and discussion In this section, weearthquakes not but only also expound providedprocess the the included whole numerical collecting process various experimental from kinds results of the data of beginning that the to were the indispensable, three transformat- end. The 2010 Yushu earthquake, located atet 33 al., 2010). In this earthquake,is 2968 the people died center and of 12 Yushu 135the was injured. a Jiegu a town which number, the number of damaged buildings and the type of damaged buildings were The o the earthquake, nearly 7017 000 000 were died, missing. more One ofsearch than the area. 370 a 000 According to were theand injured statistical 10 560 and data people from more were localnumber than injured documents, of in 3091 Dujiangyan damaged people City. died buildings TheFeng and et detailed al. casualty the (2013) number, type and the pened Tong of et was damaged at al. 07.49 (2012). buildings LT The on were 14 time April described when 2010. 2010 in The Yushu earthquake magnitude hap- was damaged buildings were describedet in al. Kuwata (2005) et were al. preparedsimilar (2005). by with the Data Iranian reported the government. byearthquake The method Kuwata way that of of attacked data-collected visual was Wenchuanepicentre interpretation. was at at The 14:28 31. LT magnitude on 12 of May 2008 2008 Wenchuan was area. The detailed casualty number, the number of damaged buildings and the type of 2.1.4 The casualty-predicting model Because the relationship between thenumber is damage not grade a of linear one function, building we did and not the use casualty the DI to replace the Besides the damage grade andindices, attributes of such buildings, as other factors theresident, we time called the software when training earthquake strengthdiscussed of happened, in escape our the study. in educational earthquake level and of the local economic level, were 2.1.3 The other factors was 10 km locatedof at the 29. provincial capitalthat of at Kerman least andof 26 975 271 km Bam people city southeast descripted were of in killed Tehran. Aghamohammadi It and et was 30 al. reported 000 (2013) injured was chosen (USGS, as 2003). the One research part 2.2 Data In our study, wequake used and three 2010 Yushu datasets, earthquake, to 2003sured evaluate Bam our model. earthquake, 2003 2008 Bam earthquake Wenchuan mea- earth- C ings, C By combining Eq. ( earlier numerical simulation results (Feng et al., 2013), we changed the The relationship between damage grade and casualty number could be descripted as 5 5 25 25 15 20 20 10 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ). = in- cd bd were N ac 65.4. 0.63. C N = = ∗∗ × 96 : 56 : C v bd = bd R bd : C 21 : 9 : 28 : 42. bc : ) were not easy 3366. ), damaged type = C 4 0.006. Using the bc = bc × bd C = ected areas. Then, k N : ff R bc 0.0073 and : 18 R ad = 0.12 and MSI bc × C k : is the casualty rate of one = R v : , we proposed a functional 4 : 1, 4 : 6. Then, it was deduced ∗∗ ac bc 42 C ad C = = ) (10) max + R C : bd ad bd k C C N ac 0.9 when buildings collapsed and : × × R 135 0.026 and RMS = bc ) and damaged type C buildings ( 0.62, MSI × bd ad = N R cc R v erent structure and material buildings in = p : ff N + 28 3366, where ad ac bc + = C equal to 0.98 when C ), we also need to calculate the value of DI 7148 7147 k 0.13. 7 was related to × bd × bc ) = C 56 7 : 3 and 1 ac C t bc ), collapsed type B buildings ( ected people in one unit of a × × R bd − = R ff value of 0.021 and RMS error of 0.001. Using this 2 erence of each point on each buildings. From the ad t v C 0.14, MSI bd ( ff ad + N r 9 p R = − N , (9) 5.12, (8) + ad + : e ), DI and MSI, the model was listed followings: ), DI of most of collapse buildings were aroud 0.9. For ac C + k 7 0.98, 1 bc 2 : 3. Then, we worked out, ac 43.6 × C + = 96 21 : 9 : 28 : 42. Because the number of occupants has high N max = × C ad × bc = e ) had some relation with the damage grade of buildings besides bd R bc v C 1 C bd R + vt 3 : 7, N N × + − square equaled to 0.998, ac : ected regions in Dujiangyan, description of Guankou town was as = N 0.015 ad 17.4Ln(MSI) ff bd C 0.41, b bc square of 0.99, + R C + R ), collapsed type C buildings ( × N = N : × : R DI bd : ac ad a e ad ad N R ad C N ( C R N 100Ln(DI) × + × : 1.63 e v ac − ad ac 0.71, ). The DI of each building was calculated. To reduce the error, we did not estimate − C = R 0.7 when buildings were damaged seriously. Parameters of Eq. ( 1 N = 1 = × = To calculate the parameters of Eq. ( = represented the percentage of a sum ac ac local survey report, we5 calculated, : 1, 135 : 18. Andless then, than 21 1. Therefore, wev made the C correlation with the number ofeach buildings, kind approximately the of distribution buildings ofIt was occupants was in written reported as thatR followings, the casualty numberkind in of Guan buildings. town Same were subscript about letters 3366. in Therefore, this paper have same meaning. From the that data regarding damage buildings, theand material the and distribution the of structurepost-earthquake of resident damaged and in buildings damaged the buildings. pointsjiangyan, Through cloud we a marked calculated pair on of heightEq. the HRSIs ( di of digital the topographic map of Du- ings. 3.1 Essential data and solving model To estimate the casualty number in a short time after an earthquake, our study required and evaluating the model. Further discussions were made based the results and find- where adjusted Eq. (10). C with adjusted function, we worked out,With MSI the combination of Eq. ( C to determine. For ( MSI the materials of buildings and relationship as followings, and MSI. According towe Eq. had ( groupedDI damaged buildings, we set DI from remote sensing with thelocal distribution GIS of database, di we canof describe the the seriously information available a followings, mathematically. As one lapsed, there were nearly notype B survival buildings space. was Therefore, very thestructure. high. casualty Type This rate C type of of collapsed buildingsconcrete buildings shear whose had walls similar structure were structure wasing with seismically was similar gravel designed very and with low. the After Low-quality we casualty reinforced combined rate the of distribution these of build- damaged buildings calculated We denoted each category by onedicated letter the number with of two collapsed subscript buildings letters.were of For damaged type instance, A. type The A other buildings 5B ( other buildings kinds ( of buildings Type A buildings were built fromwith either wood seismic or design bricks principles. and This wood.framework. type Most Compared of of buildings to them had were the similar not B other structure with types, buildings wooden the were casualty built rate from of type gravel A without was seismic lower. design Type principles. Once they col- how many casualties in one buildingdition. rather In than the in a case group of of Dujiangyan, buildings damaged with buildings similar con- were classified into 6 categories. 5 5 30 25 20 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ). bd C 0.90, 0.70, ected × ff = = bd ac ad R + bc C er their masonry × 0.90, DI 83 : 4 : 2 : 11, And ff bc = = R + ac bd 0.25. And DI R ad = : C bc × ad R erent structure and materials ad ). The result was 10 302 while : that equaled to 65.4 could be ff R bd v + bc C ) respectively, and then the value R ac 8 0.22. And DI × : C = 0.88, MSI bd ac × R R = bc ac + ) and ( R bc ( 1 bc × C v 7150 7149 × × 4 bc 0.85, MSI R = = + 0.32, MSI ad = bc sum 0.70. Because Jiegu town is the center of Yushu city, C C ad = × bd ad R + 0.32, MSI ac 45 : 5 : 35 : 16. In the scale, the = 0.95, MSI C 0.90, DI = 0.70, Then, cial report is the final number. In real disaster site, the casualty rate × ad = = ffi = bd erent kinds of buildings in the term of damaged grade and attributions, ac ac R ff bc R bd : ( ected area discussed in Kuwata et al. (2005) generally, was as the follow- × bc ff v R : × 0.95, MSI 2 0.70, DI 0.90, DI ad = ected region. The structure and materials distribution of buildings that belonged was figured out using Eq. (9). With the segmentation scales of the distribution of = ff R = = ∗∗ ac : C The bam earthquake happed to a populated area. The population density is about The casualties of Yushu earthquake were concentrated in the Jiegu town. The struc- To estimate the casualties using our model, one firstly needed to collect two paired ad bc sum ac The casualty in o increased for three or four days after an earthquake attacked. Intervention from rescue tion of an a ings in the same scale withMSI case of Dujiantyan, DI The estimated result is 22 060than while 1 the %. actual casualties were 21 924. The error is3.3 less Change of survival rate It was expected the olderis buildings heavy, brittle with and unreinforced masonry vulnerableas to to type su earthquake A shaking. buildingamounts In that of this buildings were case, that similar wereferred were referred with them similar them gravel as with structure. type low-quality Besides, B reinforced there building. concrete Using frame. were We the small visual interpretation method, the distribu- used. After consultcalculated, Fig. MSI 2, TheDI MSIs of thesethe four population kinds density isC of about damage 2 building timesit were was larger reported than that the towns actual of casualty Dujiangyan. number was Therefore, as 10 269. four The times error as was Dujiangyan. 0.03 %. Most of buildings in bam areas were not seismic design. tures of buildings instructure Jiegu and town stone are structure. mainly WeA two referred gravel types and structure that and type were stonebution B similar structure of as with respectively. type the buildings Using gravel R was the as method the of followings automatic in classification, the the scale distri- with the case of Dujiangyan, damaged building were calculated by Eqs. ( high enough to generatecould be the used. DEMs. Through Instead, twocalculated. correction the of If digital coordinate, the the map DI resolutionboth covered of was by of damaged not high buildings point pre-earthquake enough was cloud toclassification and build DEM, post-earthquake were methods satellite of the visualfound images alternatives. interpretation from and The automatic local attribution GISthe of database cluster of each through di the damagedthe coordination buildings distribution of was damaged of buildings. all After kinds of buildings was calculated. DI and MSI of each kind of tween predicting and actual resultvaluable. was 0.25. In the view of rescue, the information was of HRSIs, pre- andareas. post-earthquake In respectively, some both of situation, which the covered resolution the of a pre-earthquake satellite images was not The predicting and actual casualties were listed in Table 2. The maximum error be- To evaluate the practicalityearthquakes, of the this 2010 Yushu model, earthquake we and the also 2003 applied Bam this earthquake. model in another two the a to collapsed or damagedcasualty counts group estimated were with speculated the deduced from information history could be3.2 data. limited The in error first Application order. of of the the model of all kinds of buildings, we confirmedof the Wenchuan number earthquake, of the occupants number perthis scale of time, unit. occupants In the per the scale casualties case HRSIs unit were could equaled available. be to The 64.5. distribution estimatedcould At of by be buildings Eq. of deduced (10). di from In the extreme region situations, where only the geographical feature was similar with 5 5 25 15 20 10 10 25 20 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent. For ff erent infill walls ff ort. Compared to ff erent buildings in collapsed ff er much from actual casualties. Dur- ff cient, we did not improve the model. From 7152 7151 ffi erence between the estimation and the actual ff ectiveness of our model. From the results of 3 nu- ff c line and so on, the areas filled with damaged buildings erent. During the time when earthquake were attacking, ff ffi that even the buildings were damaged seriously the casualty number was relative lower actual situations, or theing estimating the count literature will review, di view. we Therefore, found based on most our of model,might literatures we be suggested just essential a reported to general data-input fromwork a standard might their statistical that help part point the epidemic of of researcherslet a make their report a more work regarding useful earthquake contribute andproved practical casualty. when more This report it to and would be theavailable used earthquake to in developed relief. solve countries. The Because thesome model the model literature datasets is that that were required were reported insu to the im- earthquake in developed countries, we have found merical simulation experiments, thecounts di of sometics cases of was lest the among threedensity, most all place of methods. were building It highlythe without casualties may similar, seismic in such be developed design. counties, as that If the parameters less the the should model developed, characteris- be corrected were high according used population to to estimate each step alonglutions with of timeline. small The questions.study final had Compared question some with advantages. was The otherof methods solved methods Aghamohammadi used by et in (Table al. our integrating 3), (2013). modelhammadi methods the Compared are et to similar of so- the with al. this “black the (2013), box” work And mentioned we the in illuminated parameter Aghamo- the couldmethods be meaning modified in of accordance this each with study, parameterWe the the in used actual casualties 3 our situation. could cases model. Using be to this estimated confirm in the two e days, even less time. Casualty estimation helps administratorspacts properly and respond losses. this Thisto crisis study based estimate and on limit the the its casualtyachieve mechanism im- number this of in casualty model, a proposed we short a model decomposed time with the the problem help into of several remote smaller sensing. questions To in 3.4 Advantage and disadvantage of the model other factors, such as thehad tra less weight value. The changetial of stage survival of of earthquake buildings relief. wasrescuer In helpful, were the especially in disaster in shortage areas ini- in ofdays. the earthquake, It beginning the is (Li relief very et goods important al., and changed to 2013). decide along The where crisis with are last the the for decisionwith emergency one factors, sites. of or such The two administrator. fate as Weight of the of trappers It arrival sites is of would crucial more change to relief along know goods the and rescuer change and of the survival clear rate of at road. each stage. the rescue plan. When buildingskind in damaged of state, buildings the were changeThough of nearly some survival similar rate occupants of andattacking, did each they the may not still survival escape be rate unharmed.cupants from After who remained the buildings were at attack hardly when of hurt a earthquake the could passed, high save many earthquake themselves level. oc- on were their own e state and damaged statecollapsed, were the plotted change of in survivalinstance, Figs. rate 4 of the each and survival kinds 5, rate ofthe respectively. buildings of beginning, When was gravel very buildings while di structure theremained buildings a survival remained relative rate at high ofshear level; a walls and wooden low changed the much framework level survival from with from thequake rate beginning relief di of to materials low the and quality end. personnelof Under reinforced in the each concrete limitation kind the of of disaster earth- collapsed areas, building the should change be of considered survival when rate administrators designed each kind of buildings wasabout di 40 % to 60 %was people various died or and were depended harmed onsurvival seriously the in rate a attribution decreased very and as short the timefrom time. damage went. The and rate grade Using the of the buildings. Eq. value The of (5), final the casualty changes rate calculated of survival rate of di could reduce the increasing rate of casualty. However, the change of survival rate of 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | cient recordings ffi 7154 7153 value could estimate the earthquake casualties p ected areas were essential; (3) the change of casualty ff c accident (Osaki and Minowa, 2001). And the intensity of training The authors thank the anonymous reviewers’ comments and clarification ffi square and statistically significant casualties in 14 AprilChinese). 2010 Yushu earthquake, Earthq. Eng. Eng. Vib., 31, 18–25, 2011 (in cessing functionality to supportTop. Appl., collaborative 2, and 33–46, 2009. rapid emergency response, IEEE J. Sel. object-oriented fuzzy analysis ofPhotogramm., remote 58, 239–258, sensing 2004. data for GIS-ready information, ISPRS J. Paolucci, R., and Puglia,strong-motion R.: observations, Seismol. The Res. 6 Lett., 80, April 951–966, 2009 2009. Mwquakes, 6.3 in: L’Aquila Proceedings (Central of Italy)676, the 1977. earthquake: Sixth World Conference on Earthquakeusing Engineering, the 671– capacity spectrum851–865, method: 2008. application to Barcelona, Soil Dyn. Earthq. Eng., 28, estimation for an earthquake disaster939, using 2013. neural network, Int. J. Environ. Sci. Te., 10, 931– our work can be referred easily by other peers. but also mentioned the rescue activities after the casualties had been estimated so that the primary method based on remote sensing to estimate the earthquake casualties casualties with a proposed modelconcluded and that: 3 (1) numerical the experiments. results From demonstratedR this that process, this we model with highin value developing of adjusted counties inonly a by low the error; damagedistribution (2) grade of the of occupants casualty building. inrate number The a in was attribution damaged hard of to buildings damagedBecause estimate buildings the is and earthquake important relief the to is the a design complicated of project, recuse this study plan not in only macro proposed level. of satellite images improved, the 3-Derror. shape In of a the building other canseismic be side, reconstructed design because with of of little buildings, thecaused most weak earthquake awareness by casualties of building in disaster damage.application developing prevent countries and and Based were poor prospect on of the high-resolution two remote conditions, sensing the in study estimating earthquake discussed the 4 Conclusions The first stepthe of administrators earthquake distribute the relief reliefcasualty is supplies number to and cannot rescuers know optimally. beevaluate the However, the if gained loss number the of in an of athe earthquake casualties, advantage disaster short of but which time, large earthquake help coverage, relief. the lower Remote prices, results sensing and has can short only revisit time. be As used the resolution to damage was also relative lower, manysuch casualties as were fire caused and by tra secondaryregarding disasters, earthquake relief and escaping infactors earthquake indeed was reduced also relative the higher. casualty Allto the numbers, help but build a there model were to not analyze su the situation quantitatively. 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Damaged type ACollapsed type B 241Damaged type B 1795 199.1 357 1778.6 352.5 0.01 0.21 0.01 TotalMean 3368 3294 0.02 0.10 sight damage Fall of small pieces of plaster only; heavy damage roof tiles detached; chimneys fractured at the roofline; Guankou Town Collapsed type A 975 964.1 0.01 Xingfu TownXujia Town 2846 3545 383 447 0.25 0.17 Estimating casualties and the actual casualties. Damage index and classification of damage to masonry buildings (EMS98). 01 No damage Negligible to No damage. Hairline cracks in very few walls. 23 Moderate damage4 Cracks in many walls; Substantial to5 Large and Very extensive heavy cracks damage in most walls; Serious failure of Destruction walls; Total or near-total collapse. Damage grade Damage descriptionD DamageD condition D D D D Table 2. Table 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | c Yes d Error rate Real-time b 7161 7162 Case-involved a erent methods. ff Comparison of di Framework of earthquake casualty estimation. The time used to estimateThe the number earthquake of casuslties. cases to evaluate the model. Mean (minimum, maxium). MethodAghamohammidi et al. (2013)Coburn and Spence More (1994) thanFeng 1 et week al. (2013)Method of One More this case than studying 1 week More than 5 cases less than Time-used 2 days less than 2 days 3 case 32 % One case (three subcases) 2.1 % 10 % No No 10 No % (0.1 %,25 %) Whether considering the essence of real-time estimation. a b c d Fig. 1. Table 3. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 7164 7163 erent material collapsed buildings. ff The study areas. Death rate of di Fig. 3. Fig. 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 7166 7165 erent buildings in damaged state. erent buildings in collapsed state. ff ff Change of survival rate of di Change of survival rate of di Fig. 5. Fig. 4.